Overview

Dataset statistics

Number of variables47
Number of observations512207
Missing cells87780
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory976.7 MiB
Average record size in memory2.0 KiB

Variable types

CAT29
NUM18

Warnings

Edit_dt has a high cardinality: 778 distinct values High cardinality
PIN has a high cardinality: 512180 distinct values High cardinality
DOR_C has a high cardinality: 279 distinct values High cardinality
OWNER has a high cardinality: 417046 distinct values High cardinality
ADDR_1 has a high cardinality: 379779 distinct values High cardinality
ADDR_2 has a high cardinality: 2285 distinct values High cardinality
CITY has a high cardinality: 5639 distinct values High cardinality
STATE has a high cardinality: 60 distinct values High cardinality
ZIP has a high cardinality: 102321 distinct values High cardinality
COUNTRY has a high cardinality: 96 distinct values High cardinality
SUB has a high cardinality: 10718 distinct values High cardinality
SITE_ADDR has a high cardinality: 489591 distinct values High cardinality
SITE_CITY has a high cardinality: 75 distinct values High cardinality
SITE_ZIP has a high cardinality: 517 distinct values High cardinality
LEGAL1 has a high cardinality: 58358 distinct values High cardinality
LEGAL2 has a high cardinality: 164270 distinct values High cardinality
LEGAL3 has a high cardinality: 62567 distinct values High cardinality
LEGAL4 has a high cardinality: 39210 distinct values High cardinality
DBA has a high cardinality: 21196 distinct values High cardinality
STRAP has a high cardinality: 512180 distinct values High cardinality
SD1 has a high cardinality: 189 distinct values High cardinality
S_DATE has a high cardinality: 7738 distinct values High cardinality
BLDG is highly correlated with JUST and 1 other fieldsHigh correlation
JUST is highly correlated with BLDG and 1 other fieldsHigh correlation
EFF is highly correlated with ACTHigh correlation
ACT is highly correlated with EFFHigh correlation
ASD_VAL is highly correlated with JUST and 1 other fieldsHigh correlation
MUNI is highly correlated with TAXDISTHigh correlation
TAXDIST is highly correlated with MUNIHigh correlation
S_DATE has 87780 (17.1%) missing values Missing
tBEDS is highly skewed (γ1 = 22.91204408) Skewed
tSTORIES is highly skewed (γ1 = 54.39155943) Skewed
tUNITS is highly skewed (γ1 = 66.35070617) Skewed
tBLDGS is highly skewed (γ1 = 57.03203213) Skewed
JUST is highly skewed (γ1 = 126.3349262) Skewed
LAND is highly skewed (γ1 = 78.73048924) Skewed
BLDG is highly skewed (γ1 = 104.9611111) Skewed
EXF is highly skewed (γ1 = 378.5101548) Skewed
HEAT_AR is highly skewed (γ1 = 68.24408643) Skewed
ASD_VAL is highly skewed (γ1 = 131.3984257) Skewed
TAX_VAL is highly skewed (γ1 = 66.72198269) Skewed
S_AMT is highly skewed (γ1 = 73.61751948) Skewed
ACREAGE is highly skewed (γ1 = 441.4208287) Skewed
PIN is uniformly distributed Uniform
STRAP is uniformly distributed Uniform
FOLIO has unique values Unique
tBEDS has 81938 (16.0%) zeros Zeros
tBATHS has 81336 (15.9%) zeros Zeros
tSTORIES has 50849 (9.9%) zeros Zeros
tUNITS has 80758 (15.8%) zeros Zeros
tBLDGS has 127229 (24.8%) zeros Zeros
BLDG has 54807 (10.7%) zeros Zeros
EXF has 231678 (45.2%) zeros Zeros
ACT has 50718 (9.9%) zeros Zeros
EFF has 50718 (9.9%) zeros Zeros
HEAT_AR has 50759 (9.9%) zeros Zeros
TAX_VAL has 29772 (5.8%) zeros Zeros
BASE has 220211 (43.0%) zeros Zeros
S_AMT has 87780 (17.1%) zeros Zeros

Reproduction

Analysis started2022-02-24 14:03:02.955697
Analysis finished2022-02-24 14:05:17.518440
Duration2 minutes and 14.56 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

FOLIO
Categorical

UNIQUE

Distinct512207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0000010000
 
1
0862726018
 
1
0862750300
 
1
0862750000
 
1
0862740400
 
1
Other values (512202)
512202 
ValueCountFrequency (%) 
00000100001< 0.1%
 
08627260181< 0.1%
 
08627503001< 0.1%
 
08627500001< 0.1%
 
08627404001< 0.1%
 
08627400001< 0.1%
 
08627260341< 0.1%
 
08627260331< 0.1%
 
08627260321< 0.1%
 
08627260301< 0.1%
 
08627260281< 0.1%
 
08627260261< 0.1%
 
08627260241< 0.1%
 
08627260221< 0.1%
 
08627260201< 0.1%
 
08627260161< 0.1%
 
08627252881< 0.1%
 
08627260141< 0.1%
 
08627260121< 0.1%
 
08627260101< 0.1%
 
08627260081< 0.1%
 
08627260061< 0.1%
 
08627260041< 0.1%
 
08627260021< 0.1%
 
08627253021< 0.1%
 
Other values (512182)512182> 99.9%
 
2022-02-24T09:05:18.663667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique512207 ?
Unique (%)100.0%
2022-02-24T09:05:18.735413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.999962906
Min length0

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0160716331.4%
 
14959269.7%
 
24419718.6%
 
74023817.9%
 
54016387.8%
 
43913837.6%
 
63755347.3%
 
83693707.2%
 
33535286.9%
 
92831525.5%
 
/2< 0.1%
 
N1< 0.1%
 
E1< 0.1%
 
W1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number5122046> 99.9%
 
Uppercase Letter3< 0.1%
 
Other Punctuation2< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0160716331.4%
 
14959269.7%
 
24419718.6%
 
74023817.9%
 
54016387.8%
 
43913837.6%
 
63755347.3%
 
83693707.2%
 
33535286.9%
 
92831525.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/2100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N133.3%
 
E133.3%
 
W133.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common5122048> 99.9%
 
Latin3< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0160716331.4%
 
14959269.7%
 
24419718.6%
 
74023817.9%
 
54016387.8%
 
43913837.6%
 
63755347.3%
 
83693707.2%
 
33535286.9%
 
92831525.5%
 
/2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N133.3%
 
E133.3%
 
W133.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5122051100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0160716331.4%
 
14959269.7%
 
24419718.6%
 
74023817.9%
 
54016387.8%
 
43913837.6%
 
63755347.3%
 
83693707.2%
 
33535286.9%
 
92831525.5%
 
/2< 0.1%
 
N1< 0.1%
 
E1< 0.1%
 
W1< 0.1%
 

TYPE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
512206 
dummy
 
1
ValueCountFrequency (%) 
512206> 99.9%
 
dummy1< 0.1%
 
2022-02-24T09:05:18.796209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2022-02-24T09:05:18.836378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:05:18.878743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length0
Mean length9.761678384e-06
Min length0

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
m240.0%
 
d120.0%
 
u120.0%
 
y120.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
m240.0%
 
d120.0%
 
u120.0%
 
y120.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
m240.0%
 
d120.0%
 
u120.0%
 
y120.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
m240.0%
 
d120.0%
 
u120.0%
 
y120.0%
 

Edit_dt
Categorical

HIGH CARDINALITY

Distinct778
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2019-01-25
73839 
2019-03-01
 
34602
2019-04-05
 
26046
2019-03-23
 
16138
2019-04-13
 
13734
Other values (773)
347848 
ValueCountFrequency (%) 
2019-01-257383914.4%
 
2019-03-01346026.8%
 
2019-04-05260465.1%
 
2019-03-23161383.2%
 
2019-04-13137342.7%
 
2019-03-21115792.3%
 
2019-04-19114332.2%
 
2019-03-18113712.2%
 
2019-01-08103592.0%
 
2019-01-07101802.0%
 
2019-02-2395951.9%
 
2019-01-0490491.8%
 
2018-12-3179431.6%
 
2019-03-1972201.4%
 
2019-03-2069111.3%
 
2019-02-2665381.3%
 
2019-01-1464471.3%
 
2019-01-0364231.3%
 
2019-03-1659751.2%
 
2019-01-0257961.1%
 
2019-04-1456831.1%
 
2019-02-1956491.1%
 
2019-02-2054551.1%
 
2019-01-1548440.9%
 
2019-03-0946460.9%
 
Other values (753)19475238.0%
 
2022-02-24T09:05:18.962378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2022-02-24T09:05:19.036413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0122449523.9%
 
-102441420.0%
 
190150017.6%
 
287631117.1%
 
94728999.2%
 
32024494.0%
 
41458172.8%
 
51359102.7%
 
8584961.1%
 
7427140.8%
 
6370650.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number409765680.0%
 
Dash Punctuation102441420.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0122449529.9%
 
190150022.0%
 
287631121.4%
 
947289911.5%
 
32024494.9%
 
41458173.6%
 
51359103.3%
 
8584961.4%
 
7427141.0%
 
6370650.9%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1024414100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common5122070100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0122449523.9%
 
-102441420.0%
 
190150017.6%
 
287631117.1%
 
94728999.2%
 
32024494.0%
 
41458172.8%
 
51359102.7%
 
8584961.1%
 
7427140.8%
 
6370650.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5122070100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0122449523.9%
 
-102441420.0%
 
190150017.6%
 
287631117.1%
 
94728999.2%
 
32024494.0%
 
41458172.8%
 
51359102.7%
 
8584961.1%
 
7427140.8%
 
6370650.7%
 

PIN
Categorical

HIGH CARDINALITY
UNIFORM

Distinct512180
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
 
28
A-23-33-15-ZZZ-000000-00020.0
 
1
U-29-29-21-32R-000001-00002.0
 
1
U-29-29-21-32R-000002-00006.0
 
1
U-29-29-21-32R-000002-00004.0
 
1
Other values (512175)
512175 
ValueCountFrequency (%) 
28< 0.1%
 
A-23-33-15-ZZZ-000000-00020.01< 0.1%
 
U-29-29-21-32R-000001-00002.01< 0.1%
 
U-29-29-21-32R-000002-00006.01< 0.1%
 
U-29-29-21-32R-000002-00004.01< 0.1%
 
U-29-29-21-32R-000002-00005.01< 0.1%
 
U-29-29-21-32R-000002-00003.01< 0.1%
 
U-29-29-21-32R-000002-00002.01< 0.1%
 
U-29-29-21-32R-000002-00001.01< 0.1%
 
U-29-29-21-32R-000001-00009.01< 0.1%
 
U-29-29-21-32R-000001-00008.01< 0.1%
 
U-29-29-21-32R-000001-00007.01< 0.1%
 
U-29-29-21-32R-000001-00006.01< 0.1%
 
U-29-29-21-32R-000001-00005.01< 0.1%
 
U-29-29-21-32R-000001-00004.01< 0.1%
 
U-29-29-21-32R-000001-00003.01< 0.1%
 
U-29-29-21-32R-000001-00001.01< 0.1%
 
U-29-29-21-32R-000002-00008.01< 0.1%
 
U-29-29-21-32Q-000000-B0000.01< 0.1%
 
U-29-29-21-32Q-000000-A0000.01< 0.1%
 
U-29-29-21-32Q-000000-00049.01< 0.1%
 
U-29-29-21-32Q-000000-00048.01< 0.1%
 
U-29-29-21-32Q-000000-00047.01< 0.1%
 
U-29-29-21-32Q-000000-00046.01< 0.1%
 
U-29-29-21-32Q-000000-00045.01< 0.1%
 
Other values (512155)512155> 99.9%
 
2022-02-24T09:05:20.027324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique512179 ?
Unique (%)> 99.9%
2022-02-24T09:05:20.099088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length29
Mean length28.9984147
Min length0

Overview of Unicode Properties

Unique unicode characters38
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0518780034.9%
 
-307307420.7%
 
211063287.4%
 
110461447.0%
 
35752823.9%
 
.5121793.4%
 
84758973.2%
 
94625873.1%
 
U3798122.6%
 
73000532.0%
 
42873891.9%
 
52631131.8%
 
62136481.4%
 
A1907101.3%
 
Z1735491.2%
 
B513340.3%
 
P432380.3%
 
C407040.3%
 
T342810.2%
 
D285930.2%
 
E273970.2%
 
J252120.2%
 
X247030.2%
 
V240080.2%
 
F237250.2%
 
Other values (13)2824311.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number991824166.8%
 
Dash Punctuation307307420.7%
 
Uppercase Letter13496979.1%
 
Other Punctuation5121793.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U37981228.1%
 
A19071014.1%
 
Z17354912.9%
 
B513343.8%
 
P432383.2%
 
C407043.0%
 
T342812.5%
 
D285932.1%
 
E273972.0%
 
J252121.9%
 
X247031.8%
 
V240081.8%
 
F237251.8%
 
I235181.7%
 
Q230551.7%
 
W228331.7%
 
H227661.7%
 
S223441.7%
 
R221591.6%
 
Y219851.6%
 
G218831.6%
 
N212791.6%
 
L210061.6%
 
O204531.5%
 
M200811.5%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-3073074100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0518780052.3%
 
2110632811.2%
 
1104614410.5%
 
35752825.8%
 
84758974.8%
 
94625874.7%
 
73000533.0%
 
42873892.9%
 
52631132.7%
 
62136482.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.512179100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1350349490.9%
 
Latin13496979.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
U37981228.1%
 
A19071014.1%
 
Z17354912.9%
 
B513343.8%
 
P432383.2%
 
C407043.0%
 
T342812.5%
 
D285932.1%
 
E273972.0%
 
J252121.9%
 
X247031.8%
 
V240081.8%
 
F237251.8%
 
I235181.7%
 
Q230551.7%
 
W228331.7%
 
H227661.7%
 
S223441.7%
 
R221591.6%
 
Y219851.6%
 
G218831.6%
 
N212791.6%
 
L210061.6%
 
O204531.5%
 
M200811.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
0518780038.4%
 
-307307422.8%
 
211063288.2%
 
110461447.7%
 
35752824.3%
 
.5121793.8%
 
84758973.5%
 
94625873.4%
 
73000532.2%
 
42873892.1%
 
52631131.9%
 
62136481.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII14853191100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0518780034.9%
 
-307307420.7%
 
211063287.4%
 
110461447.0%
 
35752823.9%
 
.5121793.4%
 
84758973.2%
 
94625873.1%
 
U3798122.6%
 
73000532.0%
 
42873891.9%
 
52631131.8%
 
62136481.4%
 
A1907101.3%
 
Z1735491.2%
 
B513340.3%
 
P432380.3%
 
C407040.3%
 
T342810.2%
 
D285930.2%
 
E273970.2%
 
J252120.2%
 
X247030.2%
 
V240080.2%
 
F237250.2%
 
Other values (13)2824311.9%
 

DOR_C
Categorical

HIGH CARDINALITY

Distinct279
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0100
336908 
0400
39634 
0106
34089 
0000
 
24468
0200
 
13671
Other values (274)
63437 
ValueCountFrequency (%) 
010033690865.8%
 
0400396347.7%
 
0106340896.7%
 
0000244684.8%
 
0200136712.7%
 
080045140.9%
 
090140680.8%
 
600037580.7%
 
890035650.7%
 
860027780.5%
 
000625920.5%
 
100024770.5%
 
710018500.4%
 
510014810.3%
 
870014760.3%
 
740813680.3%
 
050812430.2%
 
090210950.2%
 
16309360.2%
 
48308380.2%
 
09108350.2%
 
87107410.1%
 
17307230.1%
 
28006790.1%
 
40006780.1%
 
Other values (254)257425.0%
 
2022-02-24T09:05:20.466847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique12 ?
Unique (%)< 0.1%
2022-02-24T09:05:20.540605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.996641983
Min length0

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0145151370.9%
 
139989619.5%
 
4508952.5%
 
6472832.3%
 
2255471.2%
 
8228391.1%
 
9188290.9%
 
7132000.6%
 
384200.4%
 
570780.3%
 
H13020.1%
 
N306< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number204550099.9%
 
Uppercase Letter16080.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0145151371.0%
 
139989619.6%
 
4508952.5%
 
6472832.3%
 
2255471.2%
 
8228391.1%
 
9188290.9%
 
7132000.6%
 
384200.4%
 
570780.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H130281.0%
 
N30619.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common204550099.9%
 
Latin16080.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0145151371.0%
 
139989619.6%
 
4508952.5%
 
6472832.3%
 
2255471.2%
 
8228391.1%
 
9188290.9%
 
7132000.6%
 
384200.4%
 
570780.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
H130281.0%
 
N30619.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2047108100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0145151370.9%
 
139989619.5%
 
4508952.5%
 
6472832.3%
 
2255471.2%
 
8228391.1%
 
9188290.9%
 
7132000.6%
 
384200.4%
 
570780.3%
 
H13020.1%
 
N306< 0.1%
 

OWNER
Categorical

HIGH CARDINALITY

Distinct417046
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
** CONFIDENTIAL **
 
3267
HILLSBOROUGH COUNTY
 
2939
LENNAR HOMES LLC
 
2211
CITY OF TAMPA
 
1318
DEPT OF TRANSPORTATION
 
1269
Other values (417041)
501203 
ValueCountFrequency (%) 
** CONFIDENTIAL **32670.6%
 
HILLSBOROUGH COUNTY29390.6%
 
LENNAR HOMES LLC22110.4%
 
CITY OF TAMPA13180.3%
 
DEPT OF TRANSPORTATION12690.2%
 
WEST SHORE OASIS LLC8980.2%
 
D R HORTON INC6890.1%
 
SRP SUB LLC5340.1%
 
TAMPA ELECTRIC CO5240.1%
 
TAMPA LIFE PLAN VILLAGE INC5070.1%
 
CONCORDIA OF FLORIDA INC4670.1%
 
PULTE HOME COMPANY LLC4580.1%
 
ZILLOW HOMES PROPERTY TRUST4270.1%
 
SOUTHWEST FLORIDA WATER MANAGEMENT DISTRICT4270.1%
 
TZADIK OAKS APTS LLC4070.1%
 
2017-2 IH BORROWER LP3800.1%
 
FREEDOM VILLAGE OF SUN CITY CENTER3460.1%
 
M/I HOMES OF TAMPA LLC3320.1%
 
PARK SQUARE ENTERPRISES LLC3220.1%
 
4711 HIMES LLC3210.1%
 
CROSSWYNDE OWNER LLC3210.1%
 
ARBORS AT CARROLLWOOD OWNER LLC3200.1%
 
HOMES BY WEST BAY LLC3090.1%
 
FISHHAWK RANCH CDD3070.1%
 
LEGACY CRESCENT LLC3010.1%
 
Other values (417021)49260696.2%
 
2022-02-24T09:05:21.787612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique391945 ?
Unique (%)76.5%
2022-02-24T09:05:21.881648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length24
Mean length24.42143704
Min length0

Overview of Unicode Properties

Unique unicode characters55
Unique unicode categories10 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
167466113.4%
 
A134893410.8%
 
E11171038.9%
 
N9436477.5%
 
R8539046.8%
 
L7410135.9%
 
I6957335.6%
 
O6084494.9%
 
S5706024.6%
 
T5483484.4%
 
D5307184.2%
 
C3912883.1%
 
M3462642.8%
 
H3404452.7%
 
U2253221.8%
 
Y1974151.6%
 
G1840041.5%
 
P1807631.4%
 
B1769421.4%
 
J1511941.2%
 
K1285211.0%
 
F1184450.9%
 
W1120990.9%
 
V1057900.8%
 
Z798100.6%
 
Other values (30)1374171.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1072193485.7%
 
Space Separator167466113.4%
 
Other Punctuation542390.4%
 
Decimal Number434410.3%
 
Dash Punctuation144750.1%
 
Open Punctuation32< 0.1%
 
Close Punctuation31< 0.1%
 
Math Symbol16< 0.1%
 
Modifier Symbol1< 0.1%
 
Connector Punctuation1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A134893412.6%
 
E111710310.4%
 
N9436478.8%
 
R8539048.0%
 
L7410136.9%
 
I6957336.5%
 
O6084495.7%
 
S5706025.3%
 
T5483485.1%
 
D5307184.9%
 
C3912883.6%
 
M3462643.2%
 
H3404453.2%
 
U2253222.1%
 
Y1974151.8%
 
G1840041.7%
 
P1807631.7%
 
B1769421.7%
 
J1511941.4%
 
K1285211.2%
 
F1184451.1%
 
W1120991.0%
 
V1057901.0%
 
Z798100.7%
 
X137120.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1674661100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/3748169.1%
 
*1306824.1%
 
'21103.9%
 
&14102.6%
 
#840.2%
 
\280.1%
 
.20< 0.1%
 
,16< 0.1%
 
:9< 0.1%
 
;7< 0.1%
 
@5< 0.1%
 
%1< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-14475100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11213027.9%
 
2831819.1%
 
0686015.8%
 
433787.8%
 
730357.0%
 
327896.4%
 
520404.7%
 
817534.0%
 
616853.9%
 
914533.3%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(32100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)31100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+16100.0%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`1100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1072193485.7%
 
Common178689714.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A134893412.6%
 
E111710310.4%
 
N9436478.8%
 
R8539048.0%
 
L7410136.9%
 
I6957336.5%
 
O6084495.7%
 
S5706025.3%
 
T5483485.1%
 
D5307184.9%
 
C3912883.6%
 
M3462643.2%
 
H3404453.2%
 
U2253222.1%
 
Y1974151.8%
 
G1840041.7%
 
P1807631.7%
 
B1769421.7%
 
J1511941.4%
 
K1285211.2%
 
F1184451.1%
 
W1120991.0%
 
V1057901.0%
 
Z798100.7%
 
X137120.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
167466193.7%
 
/374812.1%
 
-144750.8%
 
*130680.7%
 
1121300.7%
 
283180.5%
 
068600.4%
 
433780.2%
 
730350.2%
 
327890.2%
 
'21100.1%
 
520400.1%
 
817530.1%
 
616850.1%
 
914530.1%
 
&14100.1%
 
#84< 0.1%
 
(32< 0.1%
 
)31< 0.1%
 
\28< 0.1%
 
.20< 0.1%
 
,16< 0.1%
 
+16< 0.1%
 
:9< 0.1%
 
;7< 0.1%
 
Other values (4)8< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII12508831100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
167466113.4%
 
A134893410.8%
 
E11171038.9%
 
N9436477.5%
 
R8539046.8%
 
L7410135.9%
 
I6957335.6%
 
O6084494.9%
 
S5706024.6%
 
T5483484.4%
 
D5307184.2%
 
C3912883.1%
 
M3462642.8%
 
H3404452.7%
 
U2253221.8%
 
Y1974151.6%
 
G1840041.5%
 
P1807631.4%
 
B1769421.4%
 
J1511941.2%
 
K1285211.0%
 
F1184450.9%
 
W1120990.9%
 
V1057900.8%
 
Z798100.6%
 
Other values (30)1374171.1%
 

ADDR_1
Categorical

HIGH CARDINALITY

Distinct379779
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
REAL ESTATE DEPT
 
2895
1717 MAIN ST STE 2000
 
2622
PO BOX 4090
 
2620
4600 W CYPRESS ST STE 200
 
2276
ATTN REAL ESTATE DIVISION
 
1296
Other values (379774)
500498 
ValueCountFrequency (%) 
REAL ESTATE DEPT28950.6%
 
1717 MAIN ST STE 200026220.5%
 
PO BOX 409026200.5%
 
4600 W CYPRESS ST STE 20022760.4%
 
ATTN REAL ESTATE DIVISION12960.3%
 
11201 MCKINLEY DR12760.2%
 
30601 AGOURA RD STE 20012270.2%
 
C/O TRICON AMERICAN HOMES LLC10280.2%
 
1 INTERNATIONAL PL STE 39008960.2%
 
8150.2%
 
12602 TELECOM DR7270.1%
 
6737 W WASHINGTON ST STE 23006580.1%
 
C/O INVITATION HOMES TAX DEPT6200.1%
 
C/O MOYER & ASSOC/SEVERN TRENT5660.1%
 
TECO ENERGY CORP TAX DEPT5370.1%
 
ATTN: ACCOUNTING5080.1%
 
120 S RIVERSIDE PLZ STE 20004870.1%
 
2662 S FALKENBURG RD4690.1%
 
134 MARWOOD RD4670.1%
 
4343 N SCOTTSDALE RD STE 3904320.1%
 
C/O YES MANAGEMENT SVCS LLC4280.1%
 
5001 PLAZA ON THE LK STE 2004270.1%
 
2379 BROAD ST4260.1%
 
C/O GREENACRE PROPERTIES INC4240.1%
 
C/O ODIN PROPERTIES LLC4070.1%
 
Other values (379754)48767395.2%
 
2022-02-24T09:05:22.900577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique340100 ?
Unique (%)66.4%
2022-02-24T09:05:22.991831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length18
Mean length18.59701644
Min length0

Overview of Unicode Properties

Unique unicode characters64
Unique unicode categories10 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
145184515.2%
 
E6079896.4%
 
R5817426.1%
 
A5269585.5%
 
14782035.0%
 
T3879104.1%
 
N3832704.0%
 
L3740803.9%
 
O3724383.9%
 
D3723063.9%
 
S3676593.9%
 
03648473.8%
 
I2937643.1%
 
22732652.9%
 
C2181592.3%
 
32062722.2%
 
41857852.0%
 
W1624401.7%
 
51608691.7%
 
P1541061.6%
 
H1495591.6%
 
V1460931.5%
 
61458961.5%
 
B1399521.5%
 
71356481.4%
 
Other values (39)8844679.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter585856161.5%
 
Decimal Number219659123.1%
 
Space Separator145184515.2%
 
Other Punctuation149580.2%
 
Dash Punctuation1628< 0.1%
 
Lowercase Letter1341< 0.1%
 
Open Punctuation298< 0.1%
 
Close Punctuation297< 0.1%
 
Modifier Symbol2< 0.1%
 
Currency Symbol1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E60798910.4%
 
R5817429.9%
 
A5269589.0%
 
T3879106.6%
 
N3832706.5%
 
L3740806.4%
 
O3724386.4%
 
D3723066.4%
 
S3676596.3%
 
I2937645.0%
 
C2181593.7%
 
W1624402.8%
 
P1541062.6%
 
H1495592.6%
 
V1460932.5%
 
B1399522.4%
 
M1200782.0%
 
G1099511.9%
 
Y1057931.8%
 
U937901.6%
 
K916211.6%
 
F519410.9%
 
X270690.5%
 
J91620.2%
 
Z73980.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1451845100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
147820321.8%
 
036484716.6%
 
227326512.4%
 
32062729.4%
 
41857858.5%
 
51608697.3%
 
61458966.6%
 
71356486.2%
 
81263085.8%
 
91194985.4%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1628100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/1235882.6%
 
&11247.5%
 
:8295.5%
 
#5853.9%
 
'500.3%
 
.110.1%
 
\1< 0.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(298100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)297100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
l27920.8%
 
i19714.7%
 
t1158.6%
 
d1047.8%
 
e1047.8%
 
r936.9%
 
h936.9%
 
p936.9%
 
s936.9%
 
v936.9%
 
n332.5%
 
a110.8%
 
y110.8%
 
c110.8%
 
u110.8%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`2100.0%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin585990261.5%
 
Common366562038.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E60798910.4%
 
R5817429.9%
 
A5269589.0%
 
T3879106.6%
 
N3832706.5%
 
L3740806.4%
 
O3724386.4%
 
D3723066.4%
 
S3676596.3%
 
I2937645.0%
 
C2181593.7%
 
W1624402.8%
 
P1541062.6%
 
H1495592.6%
 
V1460932.5%
 
B1399522.4%
 
M1200782.0%
 
G1099511.9%
 
Y1057931.8%
 
U937901.6%
 
K916211.6%
 
F519410.9%
 
X270690.5%
 
J91620.2%
 
Z73980.1%
 
Other values (16)46740.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
145184539.6%
 
147820313.0%
 
036484710.0%
 
22732657.5%
 
32062725.6%
 
41857855.1%
 
51608694.4%
 
61458964.0%
 
71356483.7%
 
81263083.4%
 
91194983.3%
 
/123580.3%
 
-1628< 0.1%
 
&1124< 0.1%
 
:829< 0.1%
 
#585< 0.1%
 
(298< 0.1%
 
)297< 0.1%
 
'50< 0.1%
 
.11< 0.1%
 
`2< 0.1%
 
$1< 0.1%
 
\1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9525522100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
145184515.2%
 
E6079896.4%
 
R5817426.1%
 
A5269585.5%
 
14782035.0%
 
T3879104.1%
 
N3832704.0%
 
L3740803.9%
 
O3724383.9%
 
D3723063.9%
 
S3676593.9%
 
03648473.8%
 
I2937643.1%
 
22732652.9%
 
C2181592.3%
 
32062722.2%
 
41857852.0%
 
W1624401.7%
 
51608691.7%
 
P1541061.6%
 
H1495591.6%
 
V1460931.5%
 
61458961.5%
 
B1399521.5%
 
71356481.4%
 
Other values (39)8844679.3%
 

ADDR_2
Categorical

HIGH CARDINALITY

Distinct2285
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
492466 
PO BOX 1110
 
2939
306 E JACKSON ST
 
1309
1508 BROOKHOLLOW DR
 
1028
210 N UNIVERSITY DR STE 702
 
774
Other values (2280)
 
13691
ValueCountFrequency (%) 
49246696.1%
 
PO BOX 111029390.6%
 
306 E JACKSON ST13090.3%
 
1508 BROOKHOLLOW DR10280.2%
 
210 N UNIVERSITY DR STE 7027740.2%
 
1717 MAIN ST STE 20006200.1%
 
4131 GUNN HWY6130.1%
 
PO BOX 1115380.1%
 
12401 N 22ND ST OFC5070.1%
 
1408 SE 17TH AVE STE B4660.1%
 
1500 MARKET ST STE 3310E4060.1%
 
23975 PARK SORRENTO STE 3003180.1%
 
4532 W KENNEDY BLVD PMB 3283070.1%
 
500 WATER ST2620.1%
 
675 3RD AVE STE 18102620.1%
 
2005 PAN AM CIR STE 120228< 0.1%
 
901 E KENNEDY BLVD224< 0.1%
 
2502 N ROCKY POINT DR STE 1050203< 0.1%
 
6554 KRYCUL AVE196< 0.1%
 
3917 RIGA BLVD190< 0.1%
 
13830 CIRCA CROSSING DR185< 0.1%
 
250 INTERNATIONAL PKWY STE 280153< 0.1%
 
9428 CAMDEN FIELD PKWY151< 0.1%
 
9300 N 16TH ST150< 0.1%
 
3900 COMMONWEALTH BLVD149< 0.1%
 
Other values (2260)75631.5%
 
2022-02-24T09:05:23.085518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1592 ?
Unique (%)0.3%
2022-02-24T09:05:23.168400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length0
Mean length0.7197949267
Min length0

Overview of Unicode Properties

Unique unicode characters43
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
6445517.5%
 
1261197.1%
 
0249426.8%
 
O209175.7%
 
E204425.5%
 
S177994.8%
 
T166484.5%
 
N135373.7%
 
R134133.6%
 
A120543.3%
 
2109033.0%
 
B98202.7%
 
D93102.5%
 
384672.3%
 
L80122.2%
 
P79012.1%
 
573262.0%
 
I64081.7%
 
759421.6%
 
K55291.5%
 
C52681.4%
 
W52141.4%
 
451411.4%
 
H50801.4%
 
X50231.4%
 
Other values (18)330149.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter20309455.1%
 
Decimal Number10106227.4%
 
Space Separator6445517.5%
 
Other Punctuation37< 0.1%
 
Dash Punctuation36< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
12611925.8%
 
02494224.7%
 
21090310.8%
 
384678.4%
 
573267.2%
 
759425.9%
 
451415.1%
 
849744.9%
 
936593.6%
 
635893.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
64455100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O2091710.3%
 
E2044210.1%
 
S177998.8%
 
T166488.2%
 
N135376.7%
 
R134136.6%
 
A120545.9%
 
B98204.8%
 
D93104.6%
 
L80123.9%
 
P79013.9%
 
I64083.2%
 
K55292.7%
 
C52682.6%
 
W52142.6%
 
H50802.5%
 
X50232.5%
 
V48282.4%
 
Y45252.2%
 
M33911.7%
 
U29991.5%
 
G21351.1%
 
J13870.7%
 
F12870.6%
 
Z1130.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-36100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
#2156.8%
 
/821.6%
 
.38.1%
 
,38.1%
 
;25.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin20309455.1%
 
Common16559044.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
6445538.9%
 
12611915.8%
 
02494215.1%
 
2109036.6%
 
384675.1%
 
573264.4%
 
759423.6%
 
451413.1%
 
849743.0%
 
936592.2%
 
635892.2%
 
-36< 0.1%
 
#21< 0.1%
 
/8< 0.1%
 
.3< 0.1%
 
,3< 0.1%
 
;2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
O2091710.3%
 
E2044210.1%
 
S177998.8%
 
T166488.2%
 
N135376.7%
 
R134136.6%
 
A120545.9%
 
B98204.8%
 
D93104.6%
 
L80123.9%
 
P79013.9%
 
I64083.2%
 
K55292.7%
 
C52682.6%
 
W52142.6%
 
H50802.5%
 
X50232.5%
 
V48282.4%
 
Y45252.2%
 
M33911.7%
 
U29991.5%
 
G21351.1%
 
J13870.7%
 
F12870.6%
 
Z1130.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368684100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
6445517.5%
 
1261197.1%
 
0249426.8%
 
O209175.7%
 
E204425.5%
 
S177994.8%
 
T166484.5%
 
N135373.7%
 
R134133.6%
 
A120543.3%
 
2109033.0%
 
B98202.7%
 
D93102.5%
 
384672.3%
 
L80122.2%
 
P79012.1%
 
573262.0%
 
I64081.7%
 
759421.6%
 
K55291.5%
 
C52681.4%
 
W52141.4%
 
451411.4%
 
H50801.4%
 
X50231.4%
 
Other values (18)330149.0%
 

CITY
Categorical

HIGH CARDINALITY

Distinct5639
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
TAMPA
233516 
RIVERVIEW
40472 
PLANT CITY
24136 
BRANDON
23546 
VALRICO
 
20802
Other values (5634)
169735 
ValueCountFrequency (%) 
TAMPA23351645.6%
 
RIVERVIEW404727.9%
 
PLANT CITY241364.7%
 
BRANDON235464.6%
 
VALRICO208024.1%
 
LUTZ175503.4%
 
SUN CITY CENTER135252.6%
 
RUSKIN109552.1%
 
APOLLO BEACH103352.0%
 
LITHIA98361.9%
 
SEFFNER88321.7%
 
WIMAUMA85111.7%
 
ODESSA72511.4%
 
DOVER54061.1%
 
GIBSONTON45660.9%
 
SCOTTSDALE36160.7%
 
DALLAS35610.7%
 
THONOTOSASSA32740.6%
 
SAINT PETERSBURG21440.4%
 
ORLANDO16000.3%
 
MIAMI14710.3%
 
WESLEY CHAPEL13360.3%
 
AGOURA HILLS13200.3%
 
CLEARWATER12750.2%
 
SANTA ANA11600.2%
 
Other values (5614)5221110.2%
 
2022-02-24T09:05:23.274555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2824 ?
Unique (%)0.6%
2022-02-24T09:05:23.365251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length5
Mean length6.915811381
Min length0

Overview of Unicode Properties

Unique unicode characters45
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A67744519.1%
 
T38774010.9%
 
P2833838.0%
 
M2657227.5%
 
I2156306.1%
 
E2084575.9%
 
R2071165.8%
 
N1724104.9%
 
L1511704.3%
 
O1437364.1%
 
V1130583.2%
 
S1080523.1%
 
C1056773.0%
 
935622.6%
 
U627091.8%
 
D597951.7%
 
W596031.7%
 
B517861.5%
 
Y461271.3%
 
H404961.1%
 
F218510.6%
 
K214900.6%
 
G184340.5%
 
Z180900.5%
 
J1545< 0.1%
 
Other values (20)72430.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter344255997.2%
 
Space Separator935622.6%
 
Decimal Number60800.2%
 
Dash Punctuation88< 0.1%
 
Other Punctuation30< 0.1%
 
Lowercase Letter8< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A67744519.7%
 
T38774011.3%
 
P2833838.2%
 
M2657227.7%
 
I2156306.3%
 
E2084576.1%
 
R2071166.0%
 
N1724105.0%
 
L1511704.4%
 
O1437364.2%
 
V1130583.3%
 
S1080523.1%
 
C1056773.1%
 
U627091.8%
 
D597951.7%
 
W596031.7%
 
B517861.5%
 
Y461271.3%
 
H404961.2%
 
F218510.6%
 
K214900.6%
 
G184340.5%
 
Z180900.5%
 
J1545< 0.1%
 
X718< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
93562100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
190114.8%
 
089714.8%
 
475212.4%
 
272511.9%
 
564410.6%
 
661710.1%
 
35709.4%
 
73756.2%
 
83165.2%
 
92834.7%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-88100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,1343.3%
 
'1033.3%
 
.413.3%
 
/310.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a450.0%
 
m225.0%
 
p225.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin344256797.2%
 
Common997602.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A67744519.7%
 
T38774011.3%
 
P2833838.2%
 
M2657227.7%
 
I2156306.3%
 
E2084576.1%
 
R2071166.0%
 
N1724105.0%
 
L1511704.4%
 
O1437364.2%
 
V1130583.3%
 
S1080523.1%
 
C1056773.1%
 
U627091.8%
 
D597951.7%
 
W596031.7%
 
B517861.5%
 
Y461271.3%
 
H404961.2%
 
F218510.6%
 
K214900.6%
 
G184340.5%
 
Z180900.5%
 
J1545< 0.1%
 
X718< 0.1%
 
Other values (4)327< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
9356293.8%
 
19010.9%
 
08970.9%
 
47520.8%
 
27250.7%
 
56440.6%
 
66170.6%
 
35700.6%
 
73750.4%
 
83160.3%
 
92830.3%
 
-880.1%
 
,13< 0.1%
 
'10< 0.1%
 
.4< 0.1%
 
/3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3542327100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A67744519.1%
 
T38774010.9%
 
P2833838.0%
 
M2657227.5%
 
I2156306.1%
 
E2084575.9%
 
R2071165.8%
 
N1724104.9%
 
L1511704.3%
 
O1437364.1%
 
V1130583.2%
 
S1080523.1%
 
C1056773.0%
 
935622.6%
 
U627091.8%
 
D597951.7%
 
W596031.7%
 
B517861.5%
 
Y461271.3%
 
H404961.1%
 
F218510.6%
 
K214900.6%
 
G184340.5%
 
Z180900.5%
 
J1545< 0.1%
 
Other values (20)72430.2%
 

STATE
Categorical

HIGH CARDINALITY

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
FL
469339 
CA
 
5963
TX
 
5440
AZ
 
4301
NY
 
3614
Other values (55)
 
23550
ValueCountFrequency (%) 
FL46933991.6%
 
CA59631.2%
 
TX54401.1%
 
AZ43010.8%
 
NY36140.7%
 
31040.6%
 
GA20550.4%
 
MA19120.4%
 
PA17820.3%
 
IL15190.3%
 
NJ15060.3%
 
VA10420.2%
 
NC9660.2%
 
WI9080.2%
 
MI8630.2%
 
OH8230.2%
 
TN6010.1%
 
MD5950.1%
 
CO4480.1%
 
IN4440.1%
 
DE4210.1%
 
CT3540.1%
 
MN3420.1%
 
NV3420.1%
 
SC3290.1%
 
Other values (35)31940.6%
 
2022-02-24T09:05:23.467655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2022-02-24T09:05:23.551229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.9878799
Min length0

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
L47117246.3%
 
F46933946.1%
 
A181401.8%
 
C83410.8%
 
N80720.8%
 
T66400.7%
 
X54400.5%
 
Z43010.4%
 
M41430.4%
 
I40540.4%
 
Y38470.4%
 
G20610.2%
 
P19260.2%
 
O17180.2%
 
J15060.1%
 
V14910.1%
 
D13740.1%
 
W12860.1%
 
H10550.1%
 
E7180.1%
 
S505< 0.1%
 
R461< 0.1%
 
K431< 0.1%
 
U185< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1018206100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L47117246.3%
 
F46933946.1%
 
A181401.8%
 
C83410.8%
 
N80720.8%
 
T66400.7%
 
X54400.5%
 
Z43010.4%
 
M41430.4%
 
I40540.4%
 
Y38470.4%
 
G20610.2%
 
P19260.2%
 
O17180.2%
 
J15060.1%
 
V14910.1%
 
D13740.1%
 
W12860.1%
 
H10550.1%
 
E7180.1%
 
S505< 0.1%
 
R461< 0.1%
 
K431< 0.1%
 
U185< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1018206100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
L47117246.3%
 
F46933946.1%
 
A181401.8%
 
C83410.8%
 
N80720.8%
 
T66400.7%
 
X54400.5%
 
Z43010.4%
 
M41430.4%
 
I40540.4%
 
Y38470.4%
 
G20610.2%
 
P19260.2%
 
O17180.2%
 
J15060.1%
 
V14910.1%
 
D13740.1%
 
W12860.1%
 
H10550.1%
 
E7180.1%
 
S505< 0.1%
 
R461< 0.1%
 
K431< 0.1%
 
U185< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1018206100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
L47117246.3%
 
F46933946.1%
 
A181401.8%
 
C83410.8%
 
N80720.8%
 
T66400.7%
 
X54400.5%
 
Z43010.4%
 
M41430.4%
 
I40540.4%
 
Y38470.4%
 
G20610.2%
 
P19260.2%
 
O17180.2%
 
J15060.1%
 
V14910.1%
 
D13740.1%
 
W12860.1%
 
H10550.1%
 
E7180.1%
 
S505< 0.1%
 
R461< 0.1%
 
K431< 0.1%
 
U185< 0.1%
 

ZIP
Categorical

HIGH CARDINALITY

Distinct102321
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
75201-4657
 
3178
 
3087
33601-1110
 
2943
85261-4090
 
2477
33607-4098
 
1593
Other values (102316)
498929 
ValueCountFrequency (%) 
75201-465731780.6%
 
30870.6%
 
33601-111029430.6%
 
85261-409024770.5%
 
33607-409815930.3%
 
33602-522313090.3%
 
91301-214813080.3%
 
33612-645612760.2%
 
92705-542610820.2%
 
02110-42038970.2%
 
33071-73207740.2%
 
33618-87257700.2%
 
33990-38017120.1%
 
53214-56506570.1%
 
33607-40996510.1%
 
33637-09356110.1%
 
33601-01116030.1%
 
30067-82615530.1%
 
33612-46215070.1%
 
10017-57044740.1%
 
33578-25534710.1%
 
60606-69954580.1%
 
16023-22454580.1%
 
78746-10534320.1%
 
34604-68994260.1%
 
Other values (102296)48450094.6%
 
2022-02-24T09:05:23.924055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique40269 ?
Unique (%)7.9%
2022-02-24T09:05:24.008078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.829484954
Min length0

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
3124780124.8%
 
651050010.1%
 
-50139310.0%
 
54950289.8%
 
14398938.7%
 
23688357.3%
 
03528267.0%
 
43473316.9%
 
73140676.2%
 
92391814.8%
 
82178704.3%
 
6< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number453333290.0%
 
Dash Punctuation50139310.0%
 
Space Separator6< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3124780127.5%
 
651050011.3%
 
549502810.9%
 
14398939.7%
 
23688358.1%
 
03528267.8%
 
43473317.7%
 
73140676.9%
 
92391815.3%
 
82178704.8%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-501393100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common5034731100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
3124780124.8%
 
651050010.1%
 
-50139310.0%
 
54950289.8%
 
14398938.7%
 
23688357.3%
 
03528267.0%
 
43473316.9%
 
73140676.2%
 
92391814.8%
 
82178704.3%
 
6< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5034731100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
3124780124.8%
 
651050010.1%
 
-50139310.0%
 
54950289.8%
 
14398938.7%
 
23688357.3%
 
03528267.0%
 
43473316.9%
 
73140676.2%
 
92391814.8%
 
82178704.3%
 
6< 0.1%
 

COUNTRY
Categorical

HIGH CARDINALITY

Distinct96
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
510202 
CANADA
 
1310
FRANCE
 
85
CHINA
 
80
UNITED KINGDOM
 
79
Other values (91)
 
451
ValueCountFrequency (%) 
51020299.6%
 
CANADA13100.3%
 
FRANCE85< 0.1%
 
CHINA80< 0.1%
 
UNITED KINGDOM79< 0.1%
 
ISRAEL67< 0.1%
 
GERMANY40< 0.1%
 
SOUTH AFRICA31< 0.1%
 
IRELAND22< 0.1%
 
HUNGARY20< 0.1%
 
CURACAO15< 0.1%
 
VENEZUELA12< 0.1%
 
AUSTRALIA11< 0.1%
 
SPAIN10< 0.1%
 
CAYMAN ISLANDS10< 0.1%
 
JAMAICA9< 0.1%
 
BRAZIL8< 0.1%
 
URUGUAY8< 0.1%
 
SINGAPORE7< 0.1%
 
TURKEY7< 0.1%
 
SAUDI ARABIA6< 0.1%
 
TRINIDAD AND TOBAGO6< 0.1%
 
BELGIUM6< 0.1%
 
NORWAY6< 0.1%
 
HONG KONG6< 0.1%
 
Other values (71)144< 0.1%
 
2022-02-24T09:05:24.091895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique38 ?
Unique (%)< 0.1%
2022-02-24T09:05:24.177608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length0
Mean length0.02655957455
Min length0

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A471034.6%
 
N190114.0%
 
C159411.7%
 
D157611.6%
 
I5554.1%
 
E4603.4%
 
R4163.1%
 
U2551.9%
 
S2121.6%
 
O2091.5%
 
T2021.5%
 
L1991.5%
 
G1971.4%
 
1901.4%
 
M1861.4%
 
H1611.2%
 
F1190.9%
 
Y1110.8%
 
K1040.8%
 
B580.4%
 
P540.4%
 
Z320.2%
 
V290.2%
 
W230.2%
 
J170.1%
 
Other values (11)340.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1338398.4%
 
Space Separator1901.4%
 
Decimal Number300.2%
 
Dash Punctuation1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A471035.2%
 
N190114.2%
 
C159411.9%
 
D157611.8%
 
I5554.1%
 
E4603.4%
 
R4163.1%
 
U2551.9%
 
S2121.6%
 
O2091.6%
 
T2021.5%
 
L1991.5%
 
G1971.5%
 
M1861.4%
 
H1611.2%
 
F1190.9%
 
Y1110.8%
 
K1040.8%
 
B580.4%
 
P540.4%
 
Z320.2%
 
V290.2%
 
W230.2%
 
J170.1%
 
X2< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
190100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0620.0%
 
3516.7%
 
8413.3%
 
2310.0%
 
5310.0%
 
9310.0%
 
1310.0%
 
6310.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1338398.4%
 
Common2211.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A471035.2%
 
N190114.2%
 
C159411.9%
 
D157611.8%
 
I5554.1%
 
E4603.4%
 
R4163.1%
 
U2551.9%
 
S2121.6%
 
O2091.6%
 
T2021.5%
 
L1991.5%
 
G1971.5%
 
M1861.4%
 
H1611.2%
 
F1190.9%
 
Y1110.8%
 
K1040.8%
 
B580.4%
 
P540.4%
 
Z320.2%
 
V290.2%
 
W230.2%
 
J170.1%
 
X2< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
19086.0%
 
062.7%
 
352.3%
 
841.8%
 
231.4%
 
531.4%
 
931.4%
 
131.4%
 
631.4%
 
-10.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII13604100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A471034.6%
 
N190114.0%
 
C159411.7%
 
D157611.6%
 
I5554.1%
 
E4603.4%
 
R4163.1%
 
U2551.9%
 
S2121.6%
 
O2091.5%
 
T2021.5%
 
L1991.5%
 
G1971.4%
 
1901.4%
 
M1861.4%
 
H1611.2%
 
F1190.9%
 
Y1110.8%
 
K1040.8%
 
B580.4%
 
P540.4%
 
Z320.2%
 
V290.2%
 
W230.2%
 
J170.1%
 
Other values (11)340.2%
 

SUB
Categorical

HIGH CARDINALITY

Distinct10718
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
ZZZ
51037 
509
 
1478
42J
 
1457
663
 
1386
3U4
 
1082
Other values (10713)
455767 
ValueCountFrequency (%) 
ZZZ5103710.0%
 
50914780.3%
 
42J14570.3%
 
66313860.3%
 
3U410820.2%
 
4PQ10640.2%
 
89N10020.2%
 
45M9520.2%
 
0019450.2%
 
1049140.2%
 
3IP8400.2%
 
3TP7890.2%
 
4547490.1%
 
3LA7280.1%
 
98M6480.1%
 
4X65840.1%
 
3D65830.1%
 
2XJ5750.1%
 
1V55730.1%
 
3DN5630.1%
 
C075590.1%
 
1RR5570.1%
 
1965500.1%
 
6065390.1%
 
3TR5350.1%
 
Other values (10693)44151886.2%
 
2022-02-24T09:05:24.284024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique393 ?
Unique (%)0.1%
2022-02-24T09:05:24.358774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.999836004
Min length0

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
Z17341811.3%
 
3935416.1%
 
0933996.1%
 
4895715.8%
 
1884335.8%
 
2869035.7%
 
5796985.2%
 
9758224.9%
 
8536433.5%
 
6527583.4%
 
7501253.3%
 
A390242.5%
 
B386322.5%
 
C318982.1%
 
P283961.8%
 
U263241.7%
 
T248321.6%
 
X245821.6%
 
J241321.6%
 
V237571.5%
 
E232271.5%
 
D230171.5%
 
Q226211.5%
 
W223181.5%
 
I220781.4%
 
Other values (11)22438814.6%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter77264450.3%
 
Decimal Number76389349.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Z17341822.4%
 
A390245.1%
 
B386325.0%
 
C318984.1%
 
P283963.7%
 
U263243.4%
 
T248323.2%
 
X245823.2%
 
J241323.1%
 
V237573.1%
 
E232273.0%
 
D230173.0%
 
Q226212.9%
 
W223182.9%
 
I220782.9%
 
Y219332.8%
 
R216082.8%
 
S214562.8%
 
H210412.7%
 
F210082.7%
 
N205282.7%
 
O199552.6%
 
L199352.6%
 
G196792.5%
 
M194062.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
39354112.2%
 
09339912.2%
 
48957111.7%
 
18843311.6%
 
28690311.4%
 
57969810.4%
 
9758229.9%
 
8536437.0%
 
6527586.9%
 
7501256.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin77264450.3%
 
Common76389349.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Z17341822.4%
 
A390245.1%
 
B386325.0%
 
C318984.1%
 
P283963.7%
 
U263243.4%
 
T248323.2%
 
X245823.2%
 
J241323.1%
 
V237573.1%
 
E232273.0%
 
D230173.0%
 
Q226212.9%
 
W223182.9%
 
I220782.9%
 
Y219332.8%
 
R216082.8%
 
S214562.8%
 
H210412.7%
 
F210082.7%
 
N205282.7%
 
O199552.6%
 
L199352.6%
 
G196792.5%
 
M194062.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
39354112.2%
 
09339912.2%
 
48957111.7%
 
18843311.6%
 
28690311.4%
 
57969810.4%
 
9758229.9%
 
8536437.0%
 
6527586.9%
 
7501256.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1536537100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
Z17341811.3%
 
3935416.1%
 
0933996.1%
 
4895715.8%
 
1884335.8%
 
2869035.7%
 
5796985.2%
 
9758224.9%
 
8536433.5%
 
6527583.4%
 
7501253.3%
 
A390242.5%
 
B386322.5%
 
C318982.1%
 
P283961.8%
 
U263241.7%
 
T248321.6%
 
X245821.6%
 
J241321.6%
 
V237571.5%
 
E232271.5%
 
D230171.5%
 
Q226211.5%
 
W223181.5%
 
I220781.4%
 
Other values (11)22438814.6%
 

SITE_ADDR
Categorical

HIGH CARDINALITY

Distinct489591
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0
 
14372
0 1
 
1353
0 AMERICAN EAGLE BLVD
 
326
611 DESTINY DR
 
93
0 0
 
71
Other values (489586)
495992 
ValueCountFrequency (%) 
0143722.8%
 
0 113530.3%
 
0 AMERICAN EAGLE BLVD3260.1%
 
611 DESTINY DR93< 0.1%
 
0 071< 0.1%
 
4201 BAYSHORE BLVD61< 0.1%
 
0 MORRIS BRIDGE RD51< 0.1%
 
3119 W DELEON ST48< 0.1%
 
0 N 301 HWY44< 0.1%
 
2001 E 2ND AVE44< 0.1%
 
3507 BAYSHORE BLVD44< 0.1%
 
0 SEDDON COVE WAY42< 0.1%
 
0 MAXWELL PARK DR38< 0.1%
 
0 THONOTOSASSA RD36< 0.1%
 
0 LITHIA PINECREST RD33< 0.1%
 
0 TAMPA PALMS BLVD31< 0.1%
 
0 LAUGHING DOVE AVE30< 0.1%
 
0 GUNN HWY30< 0.1%
 
0 WILLIAMS RD29< 0.1%
 
0 S 301 HWY29< 0.1%
 
0 UPPER CREEK DR29< 0.1%
 
0 E DR MARTIN LUTHER KING JR BLVD29< 0.1%
 
0 GALLAGHER RD29< 0.1%
 
0 N DALE MABRY HWY29< 0.1%
 
28< 0.1%
 
Other values (489566)49525896.7%
 
2022-02-24T09:05:25.583162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique486678 ?
Unique (%)95.0%
2022-02-24T09:05:25.677548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length52
Median length18
Mean length17.74600503
Min length0

Overview of Unicode Properties

Unique unicode characters63
Unique unicode categories10 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
137363415.1%
 
R5832326.4%
 
E5734616.3%
 
A5087875.6%
 
14951425.4%
 
L3760394.1%
 
D3750694.1%
 
N3620604.0%
 
03558943.9%
 
S3322773.7%
 
T3199963.5%
 
O3190873.5%
 
I2761513.0%
 
22748403.0%
 
32043462.2%
 
C1987362.2%
 
41863922.1%
 
W1625441.8%
 
51608691.8%
 
H1586871.7%
 
V1527721.7%
 
61420371.6%
 
81300911.4%
 
71294611.4%
 
B1288241.4%
 
Other values (38)8092008.9%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter551718760.7%
 
Decimal Number219688824.2%
 
Space Separator137363415.1%
 
Other Punctuation1070< 0.1%
 
Dash Punctuation701< 0.1%
 
Lowercase Letter135< 0.1%
 
Open Punctuation6< 0.1%
 
Close Punctuation5< 0.1%
 
Math Symbol1< 0.1%
 
Modifier Symbol1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
149514222.5%
 
035589416.2%
 
227484012.5%
 
32043469.3%
 
41863928.5%
 
51608697.3%
 
61420376.5%
 
81300915.9%
 
71294615.9%
 
91178165.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1373634100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R58323210.6%
 
E57346110.4%
 
A5087879.2%
 
L3760396.8%
 
D3750696.8%
 
N3620606.6%
 
S3322776.0%
 
T3199965.8%
 
O3190875.8%
 
I2761515.0%
 
C1987363.6%
 
W1625442.9%
 
H1586872.9%
 
V1527722.8%
 
B1288242.3%
 
P1128632.0%
 
G1101612.0%
 
M1094612.0%
 
Y1050411.9%
 
K926371.7%
 
U831831.5%
 
F524010.9%
 
J94360.2%
 
X56650.1%
 
Z56540.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/42639.8%
 
&32830.7%
 
#25623.9%
 
.383.6%
 
,100.9%
 
*90.8%
 
@20.2%
 
;10.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-701100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(583.3%
 
[116.7%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)5100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2317.0%
 
r1914.1%
 
i1511.1%
 
t139.6%
 
s128.9%
 
d118.1%
 
a118.1%
 
e118.1%
 
o75.2%
 
h53.7%
 
u43.0%
 
g43.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+1100.0%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin551732260.7%
 
Common357230639.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
137363438.5%
 
149514213.9%
 
035589410.0%
 
22748407.7%
 
32043465.7%
 
41863925.2%
 
51608694.5%
 
61420374.0%
 
81300913.6%
 
71294613.6%
 
91178163.3%
 
-701< 0.1%
 
/426< 0.1%
 
&328< 0.1%
 
#256< 0.1%
 
.38< 0.1%
 
,10< 0.1%
 
*9< 0.1%
 
(5< 0.1%
 
)5< 0.1%
 
@2< 0.1%
 
+1< 0.1%
 
;1< 0.1%
 
`1< 0.1%
 
[1< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R58323210.6%
 
E57346110.4%
 
A5087879.2%
 
L3760396.8%
 
D3750696.8%
 
N3620606.6%
 
S3322776.0%
 
T3199965.8%
 
O3190875.8%
 
I2761515.0%
 
C1987363.6%
 
W1625442.9%
 
H1586872.9%
 
V1527722.8%
 
B1288242.3%
 
P1128632.0%
 
G1101612.0%
 
M1094612.0%
 
Y1050411.9%
 
K926371.7%
 
U831831.5%
 
F524010.9%
 
J94360.2%
 
X56650.1%
 
Z56540.1%
 
Other values (13)30980.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9089628100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
137363415.1%
 
R5832326.4%
 
E5734616.3%
 
A5087875.6%
 
14951425.4%
 
L3760394.1%
 
D3750694.1%
 
N3620604.0%
 
03558943.9%
 
S3322773.7%
 
T3199963.5%
 
O3190873.5%
 
I2761513.0%
 
22748403.0%
 
32043462.2%
 
C1987362.2%
 
41863922.1%
 
W1625441.8%
 
51608691.8%
 
H1586871.7%
 
V1527721.7%
 
61420371.6%
 
81300911.4%
 
71294611.4%
 
B1288241.4%
 
Other values (38)8092008.9%
 

SITE_CITY
Categorical

HIGH CARDINALITY

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
TAMPA
254434 
RIVERVIEW
47938 
PLANT CITY
28705 
BRANDON
26661 
VALRICO
 
22638
Other values (70)
131831 
ValueCountFrequency (%) 
TAMPA25443449.7%
 
RIVERVIEW479389.4%
 
PLANT CITY287055.6%
 
BRANDON266615.2%
 
VALRICO226384.4%
 
LUTZ180243.5%
 
RUSKIN178263.5%
 
SUN CITY CENTER155393.0%
 
APOLLO BEACH124342.4%
 
LITHIA116972.3%
 
WIMAUMA113792.2%
 
SEFFNER108502.1%
 
TEMPLE TERRACE87351.7%
 
ODESSA74861.5%
 
GIBSONTON60481.2%
 
DOVER57591.1%
 
THONOTOSASSA41150.8%
 
5800.1%
 
Tampa5120.1%
 
Unincorporated2890.1%
 
LAKELAND251< 0.1%
 
Plant City135< 0.1%
 
Temple Terrace55< 0.1%
 
ZEPHYRHILLS33< 0.1%
 
MULBERRY13< 0.1%
 
Other values (50)71< 0.1%
 
2022-02-24T09:05:25.768911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique38 ?
Unique (%)< 0.1%
2022-02-24T09:05:25.846651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length5
Mean length6.705292977
Min length0

Overview of Unicode Properties

Unique unicode characters46
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A67122719.5%
 
T40503811.8%
 
P3044948.9%
 
M2859948.3%
 
I2214966.4%
 
R2127226.2%
 
E2096216.1%
 
N1582604.6%
 
V1243013.6%
 
L1152713.4%
 
O1118793.3%
 
C1037453.0%
 
811632.4%
 
S776292.3%
 
U631001.8%
 
W593501.7%
 
B451661.3%
 
Y442991.3%
 
D401591.2%
 
H283210.8%
 
F217020.6%
 
K180780.5%
 
Z180570.5%
 
G60540.2%
 
a1503< 0.1%
 
Other values (21)58690.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter334596897.4%
 
Space Separator811632.4%
 
Lowercase Letter73550.2%
 
Decimal Number9< 0.1%
 
Modifier Symbol3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A67122720.1%
 
T40503812.1%
 
P3044949.1%
 
M2859948.5%
 
I2214966.6%
 
R2127226.4%
 
E2096216.3%
 
N1582604.7%
 
V1243013.7%
 
L1152713.4%
 
O1118793.3%
 
C1037453.1%
 
S776292.3%
 
U631001.9%
 
W593501.8%
 
B451661.3%
 
Y442991.3%
 
D401591.2%
 
H283210.8%
 
F217020.6%
 
K180780.5%
 
Z180570.5%
 
G60540.2%
 
Q5< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
81163100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a150320.4%
 
p85611.6%
 
n7139.7%
 
r6889.4%
 
o5787.9%
 
m5677.7%
 
t5597.6%
 
e5096.9%
 
i4245.8%
 
c3444.7%
 
d2893.9%
 
l1902.6%
 
y1351.8%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`3100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3333.3%
 
5111.1%
 
6111.1%
 
9111.1%
 
8111.1%
 
2111.1%
 
0111.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin335332397.6%
 
Common811752.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A67122720.0%
 
T40503812.1%
 
P3044949.1%
 
M2859948.5%
 
I2214966.6%
 
R2127226.3%
 
E2096216.3%
 
N1582604.7%
 
V1243013.7%
 
L1152713.4%
 
O1118793.3%
 
C1037453.1%
 
S776292.3%
 
U631001.9%
 
W593501.8%
 
B451661.3%
 
Y442991.3%
 
D401591.2%
 
H283210.8%
 
F217020.6%
 
K180780.5%
 
Z180570.5%
 
G60540.2%
 
a1503< 0.1%
 
p856< 0.1%
 
Other values (12)50010.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
81163> 99.9%
 
`3< 0.1%
 
33< 0.1%
 
51< 0.1%
 
61< 0.1%
 
91< 0.1%
 
81< 0.1%
 
21< 0.1%
 
01< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3434498100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A67122719.5%
 
T40503811.8%
 
P3044948.9%
 
M2859948.3%
 
I2214966.4%
 
R2127226.2%
 
E2096216.1%
 
N1582604.6%
 
V1243013.6%
 
L1152713.4%
 
O1118793.3%
 
C1037453.0%
 
811632.4%
 
S776292.3%
 
U631001.8%
 
W593501.7%
 
B451661.3%
 
Y442991.3%
 
D401591.2%
 
H283210.8%
 
F217020.6%
 
K180780.5%
 
Z180570.5%
 
G60540.2%
 
a1503< 0.1%
 
Other values (21)58690.2%
 

SITE_ZIP
Categorical

HIGH CARDINALITY

Distinct517
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
33647
 
21502
33578
 
19761
33573
 
18451
33511
 
16837
33579
 
16410
Other values (512)
419246 
ValueCountFrequency (%) 
33647215024.2%
 
33578197613.9%
 
33573184513.6%
 
33511168373.3%
 
33579164103.2%
 
33624145672.8%
 
33604143862.8%
 
33619142472.8%
 
33615142342.8%
 
33610139542.7%
 
33614138982.7%
 
33570138712.7%
 
33611137282.7%
 
33612130622.6%
 
33617122732.4%
 
33572121662.4%
 
33547115512.3%
 
33594115162.2%
 
33598109212.1%
 
33596107932.1%
 
33629107382.1%
 
33584106802.1%
 
33626104522.0%
 
33569103372.0%
 
3361896841.9%
 
Other values (492)17218833.6%
 
2022-02-24T09:05:25.928379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique256 ?
Unique (%)< 0.1%
2022-02-24T09:05:25.999346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length5
Mean length5.0153805
Min length0

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
3108347142.2%
 
635017013.6%
 
530768912.0%
 
11754896.8%
 
71530746.0%
 
91206234.7%
 
41202764.7%
 
01007863.9%
 
2889443.5%
 
8628352.4%
 
-55480.2%
 
_8< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number256335799.8%
 
Dash Punctuation55480.2%
 
Connector Punctuation8< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3108347142.3%
 
635017013.7%
 
530768912.0%
 
11754896.8%
 
71530746.0%
 
91206234.7%
 
41202764.7%
 
01007863.9%
 
2889443.5%
 
8628352.5%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-5548100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_8100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2568913100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
3108347142.2%
 
635017013.6%
 
530768912.0%
 
11754896.8%
 
71530746.0%
 
91206234.7%
 
41202764.7%
 
01007863.9%
 
2889443.5%
 
8628352.4%
 
-55480.2%
 
_8< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2568913100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
3108347142.2%
 
635017013.6%
 
530768912.0%
 
11754896.8%
 
71530746.0%
 
91206234.7%
 
41202764.7%
 
01007863.9%
 
2889443.5%
 
8628352.4%
 
-55480.2%
 
_8< 0.1%
 

LEGAL1
Categorical

HIGH CARDINALITY

Distinct58358
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
DAVIS ISLANDS PB10 PG52 TO 57 AND PB17 PG5 TO 9
 
1468
PORT TAMPA CITY MAP
 
1454
SOUTH TAMPA SUBDIVISION
 
1344
PALMA CEIA PARK
 
1082
MAC FARLANES REV MAP OF ADDITIONS TO WEST TAMPA
 
1053
Other values (58353)
505806 
ValueCountFrequency (%) 
DAVIS ISLANDS PB10 PG52 TO 57 AND PB17 PG5 TO 914680.3%
 
PORT TAMPA CITY MAP14540.3%
 
SOUTH TAMPA SUBDIVISION13440.3%
 
PALMA CEIA PARK10820.2%
 
MAC FARLANES REV MAP OF ADDITIONS TO WEST TAMPA10530.2%
 
GRANDE OASIS AT CARROLLWOOD10020.2%
 
SULPHUR SPRINGS ADDITION9510.2%
 
KEYSTONE PARK COLONY9410.2%
 
TAMPA'S NORTH SIDE COUNTRY CLUB AREA UNIT NO 39110.2%
 
DREW PARK RE PLAT OF8230.2%
 
VIRGINIA PARK7870.2%
 
TAMPA OVERLOOK7480.1%
 
BEACH PARK7260.1%
 
GOLFLAND OF TAMPA'S NORTH SIDE COUNTRY CLUB AREA6770.1%
 
TEMPLE TERRACES6740.1%
 
AYERSWORTH GLEN6480.1%
 
EAST TAMPA BLOCKS 1 TO 425840.1%
 
WIMAUMA TOWN OF REVISED MAP5740.1%
 
RUSKIN CITY MAP OF5700.1%
 
TAMPA'S NORTH SIDE COUNTRY CLUB AREA UNIT NO 15620.1%
 
SHELL COVE PHASE 15590.1%
 
FLORIDA GARDEN LANDS REVISED MAP OF5490.1%
 
SUN CITY5480.1%
 
CARIBBEAN ISLES RESIDENTIAL COOPERATIVE5370.1%
 
MARYLAND MANOR REVISED PLAT5350.1%
 
Other values (58333)49190096.0%
 
2022-02-24T09:05:26.176349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique46207 ?
Unique (%)9.0%
2022-02-24T09:05:26.280044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length25
Mean length26.64290023
Min length0

Overview of Unicode Properties

Unique unicode characters51
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
214610015.7%
 
E10504687.7%
 
A9672997.1%
 
O8739606.4%
 
N8144076.0%
 
S8125526.0%
 
I7548745.5%
 
T7432515.4%
 
R6498014.8%
 
L4754293.5%
 
C3803302.8%
 
D3608422.6%
 
H3563102.6%
 
P3395332.5%
 
U3032512.2%
 
F2884872.1%
 
M2687662.0%
 
12330011.7%
 
W2190051.6%
 
B2128871.6%
 
V1811621.3%
 
G1786571.3%
 
K1700011.2%
 
21473891.1%
 
Y1127060.8%
 
Other values (26)6062124.4%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1053875977.2%
 
Space Separator214610015.7%
 
Decimal Number7967345.8%
 
Other Punctuation1416861.0%
 
Dash Punctuation232530.2%
 
Open Punctuation78< 0.1%
 
Close Punctuation70< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
123300129.2%
 
214738918.5%
 
411064013.9%
 
3773759.7%
 
0609497.6%
 
5568717.1%
 
6308713.9%
 
7304343.8%
 
8259833.3%
 
9232212.9%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2146100100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E105046810.0%
 
A9672999.2%
 
O8739608.3%
 
N8144077.7%
 
S8125527.7%
 
I7548747.2%
 
T7432517.1%
 
R6498016.2%
 
L4754294.5%
 
C3803303.6%
 
D3608423.4%
 
H3563103.4%
 
P3395333.2%
 
U3032512.9%
 
F2884872.7%
 
M2687662.6%
 
W2190052.1%
 
B2128872.0%
 
V1811621.7%
 
G1786571.7%
 
K1700011.6%
 
Y1127061.1%
 
X94290.1%
 
J73260.1%
 
Z4449< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-23253100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/9212265.0%
 
'2541017.9%
 
.1941813.7%
 
&36002.5%
 
:9620.7%
 
,1450.1%
 
#22< 0.1%
 
@3< 0.1%
 
*2< 0.1%
 
;1< 0.1%
 
\1< 0.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(78100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)70100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1053875977.2%
 
Common310792122.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
214610069.1%
 
12330017.5%
 
21473894.7%
 
41106403.6%
 
/921223.0%
 
3773752.5%
 
0609492.0%
 
5568711.8%
 
6308711.0%
 
7304341.0%
 
8259830.8%
 
'254100.8%
 
-232530.7%
 
9232210.7%
 
.194180.6%
 
&36000.1%
 
:962< 0.1%
 
,145< 0.1%
 
(78< 0.1%
 
)70< 0.1%
 
#22< 0.1%
 
@3< 0.1%
 
*2< 0.1%
 
;1< 0.1%
 
\1< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E105046810.0%
 
A9672999.2%
 
O8739608.3%
 
N8144077.7%
 
S8125527.7%
 
I7548747.2%
 
T7432517.1%
 
R6498016.2%
 
L4754294.5%
 
C3803303.6%
 
D3608423.4%
 
H3563103.4%
 
P3395333.2%
 
U3032512.9%
 
F2884872.7%
 
M2687662.6%
 
W2190052.1%
 
B2128872.0%
 
V1811621.7%
 
G1786571.7%
 
K1700011.6%
 
Y1127061.1%
 
X94290.1%
 
J73260.1%
 
Z4449< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII13646680100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
214610015.7%
 
E10504687.7%
 
A9672997.1%
 
O8739606.4%
 
N8144076.0%
 
S8125526.0%
 
I7548745.5%
 
T7432515.4%
 
R6498014.8%
 
L4754293.5%
 
C3803302.8%
 
D3608422.6%
 
H3563102.6%
 
P3395332.5%
 
U3032512.2%
 
F2884872.1%
 
M2687662.0%
 
12330011.7%
 
W2190051.6%
 
B2128871.6%
 
V1811621.3%
 
G1786571.3%
 
K1700011.2%
 
21473891.1%
 
Y1127060.8%
 
Other values (26)6062124.4%
 

LEGAL2
Categorical

HIGH CARDINALITY

Distinct164270
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
 
4362
LOT 2
 
3061
LOT 1
 
3042
LOT 3
 
2779
LOT 4
 
2342
Other values (164265)
496621 
ValueCountFrequency (%) 
43620.9%
 
LOT 230610.6%
 
LOT 130420.6%
 
LOT 327790.5%
 
LOT 423420.5%
 
LOT 520390.4%
 
LOT 618830.4%
 
LOT 717160.3%
 
LOT 815990.3%
 
LOT 914600.3%
 
LOT 1013720.3%
 
LOT 4 BLOCK 113350.3%
 
LOT 1 BLOCK 113340.3%
 
LOT 3 BLOCK 113330.3%
 
LOT 2 BLOCK 113110.3%
 
LOT 5 BLOCK 112940.3%
 
LOT 3 BLOCK 212940.3%
 
LOT 4 BLOCK 212890.3%
 
LOT 2 BLOCK 212870.3%
 
LOT 1 BLOCK 212850.3%
 
LOT 6 BLOCK 112560.2%
 
LOT 1112530.2%
 
LOT 5 BLOCK 212320.2%
 
LOT 6 BLOCK 212000.2%
 
LOT 1211800.2%
 
Other values (164245)46866991.5%
 
2022-02-24T09:05:26.771866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique144947 ?
Unique (%)28.3%
2022-02-24T09:05:26.877283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length14
Mean length20.38699979
Min length0

Overview of Unicode Properties

Unique unicode characters51
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
239288822.9%
 
O9824469.4%
 
L8492288.1%
 
T7680017.4%
 
14607294.4%
 
N4082373.9%
 
C3797403.6%
 
B3451323.3%
 
22927552.8%
 
F2909782.8%
 
K2902522.8%
 
E2720572.6%
 
A2492722.4%
 
S2407722.3%
 
D2004351.9%
 
31954991.9%
 
41916011.8%
 
51595701.5%
 
I1582581.5%
 
01521601.5%
 
R1281681.2%
 
61184921.1%
 
71025961.0%
 
8995691.0%
 
W958310.9%
 
Other values (26)6176985.9%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter602173257.7%
 
Space Separator239288822.9%
 
Decimal Number185986417.8%
 
Other Punctuation1466611.4%
 
Dash Punctuation206990.2%
 
Open Punctuation269< 0.1%
 
Close Punctuation250< 0.1%
 
Currency Symbol1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O98244616.3%
 
L84922814.1%
 
T76800112.8%
 
N4082376.8%
 
C3797406.3%
 
B3451325.7%
 
F2909784.8%
 
K2902524.8%
 
E2720574.5%
 
A2492724.1%
 
S2407724.0%
 
D2004353.3%
 
I1582582.6%
 
R1281682.1%
 
W958311.6%
 
U896211.5%
 
M769131.3%
 
G602081.0%
 
H427370.7%
 
P354310.6%
 
V286860.5%
 
Y260500.4%
 
J1397< 0.1%
 
X960< 0.1%
 
Q560< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2392888100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
146072924.8%
 
229275515.7%
 
319549910.5%
 
419160110.3%
 
51595708.6%
 
01521608.2%
 
61184926.4%
 
71025965.5%
 
8995695.4%
 
9868934.7%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/8314156.7%
 
.4477430.5%
 
&107007.3%
 
:34342.3%
 
,28461.9%
 
'13720.9%
 
%3160.2%
 
#61< 0.1%
 
;16< 0.1%
 
*1< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-20699100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(269100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)250100.0%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin602173257.7%
 
Common442063242.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
O98244616.3%
 
L84922814.1%
 
T76800112.8%
 
N4082376.8%
 
C3797406.3%
 
B3451325.7%
 
F2909784.8%
 
K2902524.8%
 
E2720574.5%
 
A2492724.1%
 
S2407724.0%
 
D2004353.3%
 
I1582582.6%
 
R1281682.1%
 
W958311.6%
 
U896211.5%
 
M769131.3%
 
G602081.0%
 
H427370.7%
 
P354310.6%
 
V286860.5%
 
Y260500.4%
 
J1397< 0.1%
 
X960< 0.1%
 
Q560< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
239288854.1%
 
146072910.4%
 
22927556.6%
 
31954994.4%
 
41916014.3%
 
51595703.6%
 
01521603.4%
 
61184922.7%
 
71025962.3%
 
8995692.3%
 
9868932.0%
 
/831411.9%
 
.447741.0%
 
-206990.5%
 
&107000.2%
 
:34340.1%
 
,28460.1%
 
'1372< 0.1%
 
%316< 0.1%
 
(269< 0.1%
 
)250< 0.1%
 
#61< 0.1%
 
;16< 0.1%
 
*1< 0.1%
 
$1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII10442364100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
239288822.9%
 
O9824469.4%
 
L8492288.1%
 
T7680017.4%
 
14607294.4%
 
N4082373.9%
 
C3797403.6%
 
B3451323.3%
 
22927552.8%
 
F2909782.8%
 
K2902522.8%
 
E2720572.6%
 
A2492722.4%
 
S2407722.3%
 
D2004351.9%
 
31954991.9%
 
41916011.8%
 
51595701.5%
 
I1582581.5%
 
01521601.5%
 
R1281681.2%
 
61184921.1%
 
71025961.0%
 
8995691.0%
 
W958310.9%
 
Other values (26)6176985.9%
 

LEGAL3
Categorical

HIGH CARDINALITY

Distinct62567
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
409558 
ELEMENTS
 
5569
AND AN UNDIV INT IN COMMON ELEMENTS
 
3424
ABUTTING THEREON
 
653
THEREON
 
605
Other values (62562)
92398 
ValueCountFrequency (%) 
40955880.0%
 
ELEMENTS55691.1%
 
AND AN UNDIV INT IN COMMON ELEMENTS34240.7%
 
ABUTTING THEREON6530.1%
 
THEREON6050.1%
 
1/4 OF SW 1/4 AND S TO SW COR W TO SW COR OF4470.1%
 
THEREOF4260.1%
 
TYPE B3640.1%
 
TYPE 1/13370.1%
 
TYPE A3300.1%
 
JOHN KNOX VILLAGE PHASE TWO3080.1%
 
ABUTTING THEREOF2750.1%
 
BLOCK 2250< 0.1%
 
FT TO BEG250< 0.1%
 
COMMON ELEMENTS230< 0.1%
 
SE 1/4 W 36 FT TO POB S 473.74 FT W 94 FT229< 0.1%
 
BLOCK 3216< 0.1%
 
AND S 623.90 FT TO BEG215< 0.1%
 
BLOCK 1211< 0.1%
 
TO BEG210< 0.1%
 
TYPE 2/2207< 0.1%
 
BLOCK 4205< 0.1%
 
1/199 INT IN COMMON AND LIMITED COMMON ELEMENTS195< 0.1%
 
BLOCK 5189< 0.1%
 
TYPE HAMPTON174< 0.1%
 
Other values (62542)8713017.0%
 
2022-02-24T09:05:27.132860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique57723 ?
Unique (%)11.3%
2022-02-24T09:05:27.243489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length0
Mean length6.083511159
Min length0

Overview of Unicode Properties

Unique unicode characters53
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
70813022.7%
 
T2103996.8%
 
N1900406.1%
 
E1817215.8%
 
O1810515.8%
 
F1261264.0%
 
S1149963.7%
 
L1034633.3%
 
A921343.0%
 
1914982.9%
 
R816672.6%
 
D813332.6%
 
I718422.3%
 
C674222.2%
 
2604391.9%
 
0601831.9%
 
W544451.7%
 
B523661.7%
 
G503821.6%
 
4501641.6%
 
M474661.5%
 
5462751.5%
 
3450051.4%
 
.363251.2%
 
H360641.2%
 
Other values (28)2750818.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter186176759.7%
 
Space Separator70813022.7%
 
Decimal Number46740115.0%
 
Other Punctuation726472.3%
 
Dash Punctuation56380.2%
 
Open Punctuation218< 0.1%
 
Close Punctuation214< 0.1%
 
Math Symbol2< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
19149819.6%
 
26043912.9%
 
06018312.9%
 
45016410.7%
 
5462759.9%
 
3450059.6%
 
8307116.6%
 
6297526.4%
 
7269775.8%
 
9263975.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
708130100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.3632550.0%
 
/2927140.3%
 
&29684.1%
 
:13911.9%
 
,10651.5%
 
'9011.2%
 
%5450.8%
 
#1670.2%
 
;13< 0.1%
 
!1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T21039911.3%
 
N19004010.2%
 
E1817219.8%
 
O1810519.7%
 
F1261266.8%
 
S1149966.2%
 
L1034635.6%
 
A921344.9%
 
R816674.4%
 
D813334.4%
 
I718423.9%
 
C674223.6%
 
W544452.9%
 
B523662.8%
 
G503822.7%
 
M474662.5%
 
H360641.9%
 
U350481.9%
 
Y253861.4%
 
P249261.3%
 
K195631.1%
 
V116850.6%
 
X902< 0.1%
 
J865< 0.1%
 
Z262< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-5638100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(21799.5%
 
[10.5%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)214100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+150.0%
 
=150.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin186176759.7%
 
Common125425040.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
70813056.5%
 
1914987.3%
 
2604394.8%
 
0601834.8%
 
4501644.0%
 
5462753.7%
 
3450053.6%
 
.363252.9%
 
8307112.4%
 
6297522.4%
 
/292712.3%
 
7269772.2%
 
9263972.1%
 
-56380.4%
 
&29680.2%
 
:13910.1%
 
,10650.1%
 
'9010.1%
 
%545< 0.1%
 
(217< 0.1%
 
)214< 0.1%
 
#167< 0.1%
 
;13< 0.1%
 
[1< 0.1%
 
+1< 0.1%
 
Other values (2)2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
T21039911.3%
 
N19004010.2%
 
E1817219.8%
 
O1810519.7%
 
F1261266.8%
 
S1149966.2%
 
L1034635.6%
 
A921344.9%
 
R816674.4%
 
D813334.4%
 
I718423.9%
 
C674223.6%
 
W544452.9%
 
B523662.8%
 
G503822.7%
 
M474662.5%
 
H360641.9%
 
U350481.9%
 
Y253861.4%
 
P249261.3%
 
K195631.1%
 
V116850.6%
 
X902< 0.1%
 
J865< 0.1%
 
Z262< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3116017100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
70813022.7%
 
T2103996.8%
 
N1900406.1%
 
E1817215.8%
 
O1810515.8%
 
F1261264.0%
 
S1149963.7%
 
L1034633.3%
 
A921343.0%
 
1914982.9%
 
R816672.6%
 
D813332.6%
 
I718422.3%
 
C674222.2%
 
2604391.9%
 
0601831.9%
 
W544451.7%
 
B523661.7%
 
G503821.6%
 
4501641.6%
 
M474661.5%
 
5462751.5%
 
3450051.4%
 
.363251.2%
 
H360641.2%
 
Other values (28)2750818.8%
 

LEGAL4
Categorical

HIGH CARDINALITY

Distinct39210
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
460920 
THAT PORTION OF PROPERTY LEGALLY DESCRIBED UNDER
 
571
NW 1/4 OF SW 1/4 N TO NW COR OF NW 1/4 OF SW 1/4
 
447
LIMITED COMMON ELEMENTS
 
336
TYPE A
 
282
Other values (39205)
49651 
ValueCountFrequency (%) 
46092090.0%
 
THAT PORTION OF PROPERTY LEGALLY DESCRIBED UNDER5710.1%
 
NW 1/4 OF SW 1/4 N TO NW COR OF NW 1/4 OF SW 1/44470.1%
 
LIMITED COMMON ELEMENTS3360.1%
 
TYPE A2820.1%
 
S 45 DEG W 28.28 FT W 219.97 FT N 494.40 FT229< 0.1%
 
1/223% OWNERSHIP IN THE COMMON ELEMENTS AND213< 0.1%
 
ELEMENTS AND LIMITED COMMON ELEMENTS209< 0.1%
 
ELEMENTS201< 0.1%
 
4.55 PERCENTAGE OF COMMON ELEMENTS151< 0.1%
 
3.78 PERCENTAGE OF COMMON ELEMENTS145< 0.1%
 
S 80 DEG 02 MIN 36 SEC W 140.25 FT ALG142< 0.1%
 
TO POB135< 0.1%
 
TYPE D130< 0.1%
 
TYPE 1/1127< 0.1%
 
TO BEG126< 0.1%
 
TYPE B126< 0.1%
 
TYPE 2/2124< 0.1%
 
1/123RD UNDIVIDED SHARE OF COMMON ELEMENTS AND123< 0.1%
 
AND SURPLUS116< 0.1%
 
SEE OR 6237/1356-1381 FOR MASTER OCCUPANCY116< 0.1%
 
3.78 PERCENTAGE OF COMMON INTEREST105< 0.1%
 
4.55 PERCENTAGE OF COMMON INTEREST104< 0.1%
 
.00325% UNDIV SHARE OF THE COMMON ELEMENTS100< 0.1%
 
1/24TH UNDIVIDED SHARE OF OWNERSHIP OF COMMON96< 0.1%
 
Other values (39185)468339.1%
 
2022-02-24T09:05:27.438564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique37420 ?
Unique (%)7.3%
2022-02-24T09:05:27.551050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length0
Mean length3.891141667
Min length0

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
47276323.7%
 
T1236466.2%
 
E1202536.0%
 
N1119295.6%
 
O1003115.0%
 
F805464.0%
 
S731053.7%
 
1573852.9%
 
D557252.8%
 
R522442.6%
 
L474082.4%
 
0466682.3%
 
A435952.2%
 
I435552.2%
 
C427832.1%
 
2410642.1%
 
4381321.9%
 
W373821.9%
 
G365741.8%
 
M364521.8%
 
5350401.8%
 
3335581.7%
 
.312381.6%
 
H275771.4%
 
8257421.3%
 
Other values (27)1783959.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter112372856.4%
 
Space Separator47276323.7%
 
Decimal Number34085517.1%
 
Other Punctuation527202.6%
 
Dash Punctuation26340.1%
 
Open Punctuation192< 0.1%
 
Close Punctuation176< 0.1%
 
Math Symbol1< 0.1%
 
Modifier Symbol1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T12364611.0%
 
E12025310.7%
 
N11192910.0%
 
O1003118.9%
 
F805467.2%
 
S731056.5%
 
D557255.0%
 
R522444.6%
 
L474084.2%
 
A435953.9%
 
I435553.9%
 
C427833.8%
 
W373823.3%
 
G365743.3%
 
M364523.2%
 
H275772.5%
 
B255602.3%
 
P216551.9%
 
Y187941.7%
 
U132071.2%
 
V60950.5%
 
K42470.4%
 
X5790.1%
 
Z241< 0.1%
 
J193< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
472763100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
15738516.8%
 
04666813.7%
 
24106412.0%
 
43813211.2%
 
53504010.3%
 
3335589.8%
 
8257427.6%
 
6216286.3%
 
9210296.2%
 
7206096.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2634100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.3123859.3%
 
/1705132.3%
 
&15412.9%
 
%11752.2%
 
,5961.1%
 
:5931.1%
 
'4560.9%
 
#620.1%
 
;6< 0.1%
 
?2< 0.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)176100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(192100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
=1100.0%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin112372856.4%
 
Common86934243.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
T12364611.0%
 
E12025310.7%
 
N11192910.0%
 
O1003118.9%
 
F805467.2%
 
S731056.5%
 
D557255.0%
 
R522444.6%
 
L474084.2%
 
A435953.9%
 
I435553.9%
 
C427833.8%
 
W373823.3%
 
G365743.3%
 
M364523.2%
 
H275772.5%
 
B255602.3%
 
P216551.9%
 
Y187941.7%
 
U132071.2%
 
V60950.5%
 
K42470.4%
 
X5790.1%
 
Z241< 0.1%
 
J193< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
47276354.4%
 
1573856.6%
 
0466685.4%
 
2410644.7%
 
4381324.4%
 
5350404.0%
 
3335583.9%
 
.312383.6%
 
8257423.0%
 
6216282.5%
 
9210292.4%
 
7206092.4%
 
/170512.0%
 
-26340.3%
 
&15410.2%
 
%11750.1%
 
,5960.1%
 
:5930.1%
 
'4560.1%
 
(192< 0.1%
 
)176< 0.1%
 
#62< 0.1%
 
;6< 0.1%
 
?2< 0.1%
 
=1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1993070100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
47276323.7%
 
T1236466.2%
 
E1202536.0%
 
N1119295.6%
 
O1003115.0%
 
F805464.0%
 
S731053.7%
 
1573852.9%
 
D557252.8%
 
R522442.6%
 
L474082.4%
 
0466682.3%
 
A435952.2%
 
I435552.2%
 
C427832.1%
 
2410642.1%
 
4381321.9%
 
W373821.9%
 
G365741.8%
 
M364521.8%
 
5350401.8%
 
3335581.7%
 
.312381.6%
 
H275771.4%
 
8257421.3%
 
Other values (27)1783959.0%
 

DBA
Categorical

HIGH CARDINALITY

Distinct21196
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
476677 
VACANT
 
867
VACANT LOT
 
800
FLETCHER EXHOME FOR AGED
 
499
VACANT LAND
 
457
Other values (21191)
 
32907
ValueCountFrequency (%) 
47667793.1%
 
VACANT8670.2%
 
VACANT LOT8000.2%
 
FLETCHER EXHOME FOR AGED4990.1%
 
VACANT LAND4570.1%
 
UNIVERSITY VILLAGE4480.1%
 
RETENTION3970.1%
 
DRAINAGE AREA2910.1%
 
WHISPERING OAKS CONDOMINIUM2910.1%
 
EASEMENT239< 0.1%
 
COMMON AREA239< 0.1%
 
SEACOAST AT UPTOWN OAKS229< 0.1%
 
THE GALLERY AT BAYPORT CONDOMINIUM174< 0.1%
 
DRAINAGE164< 0.1%
 
CONSERVATION149< 0.1%
 
FREEDOM VILLAGE 3142< 0.1%
 
I-275 ROAD R/W130< 0.1%
 
CONSERVATION AREA130< 0.1%
 
LIFT STATION116< 0.1%
 
RIGHT OF WAY110< 0.1%
 
I-4 ROAD R/W98< 0.1%
 
CROSSTOWN ROAD R/W89< 0.1%
 
CANTERBURY TOWERS88< 0.1%
 
CROSSTOWN EXPRWY ROAD R/W82< 0.1%
 
WETLANDS79< 0.1%
 
Other values (21171)292225.7%
 
2022-02-24T09:05:27.730855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique18925 ?
Unique (%)3.7%
2022-02-24T09:05:27.845435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length0
Mean length1.370139416
Min length0

Overview of Unicode Properties

Unique unicode characters83
Unique unicode categories12 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
7575310.8%
 
A630519.0%
 
E608288.7%
 
R495567.1%
 
T453786.5%
 
O448166.4%
 
N422016.0%
 
I404555.8%
 
S380395.4%
 
C316694.5%
 
L298584.3%
 
M183462.6%
 
D176562.5%
 
P175452.5%
 
H169782.4%
 
U160692.3%
 
G127471.8%
 
F119461.7%
 
V101261.4%
 
Y92171.3%
 
B91471.3%
 
W89291.3%
 
K80451.1%
 
/24960.4%
 
X20110.3%
 
Other values (58)189332.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter60732486.5%
 
Space Separator7575310.8%
 
Decimal Number84041.2%
 
Other Punctuation77711.1%
 
Dash Punctuation17050.2%
 
Open Punctuation323< 0.1%
 
Close Punctuation321< 0.1%
 
Lowercase Letter124< 0.1%
 
Modifier Symbol47< 0.1%
 
Math Symbol18< 0.1%
 
Currency Symbol3< 0.1%
 
Other Number2< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A6305110.4%
 
E6082810.0%
 
R495568.2%
 
T453787.5%
 
O448167.4%
 
N422016.9%
 
I404556.7%
 
S380396.3%
 
C316695.2%
 
L298584.9%
 
M183463.0%
 
D176562.9%
 
P175452.9%
 
H169782.8%
 
U160692.6%
 
G127472.1%
 
F119462.0%
 
V101261.7%
 
Y92171.5%
 
B91471.5%
 
W89291.5%
 
K80451.3%
 
X20110.3%
 
Z12150.2%
 
J9440.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
75753100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/249632.1%
 
&174522.5%
 
.153819.8%
 
'101313.0%
 
#5857.5%
 
,2633.4%
 
@911.2%
 
:160.2%
 
?80.1%
 
*50.1%
 
\50.1%
 
!3< 0.1%
 
;2< 0.1%
 
%1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0171920.5%
 
1108412.9%
 
299311.8%
 
98289.9%
 
78019.5%
 
57609.0%
 
37569.0%
 
47478.9%
 
63784.5%
 
83384.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1705100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(323100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)321100.0%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`47100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+18100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1713.7%
 
i129.7%
 
e129.7%
 
r97.3%
 
l97.3%
 
n97.3%
 
s86.5%
 
t86.5%
 
o64.8%
 
c54.0%
 
u43.2%
 
b43.2%
 
d32.4%
 
f21.6%
 
g21.6%
 
m21.6%
 
ó21.6%
 
é21.6%
 
p21.6%
 
y21.6%
 
z10.8%
 
h10.8%
 
w10.8%
 
v10.8%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
£266.7%
 
$133.3%
 

Most frequent Other Number characters

ValueCountFrequency (%) 
¼2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin60744886.6%
 
Common9434713.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A6305110.4%
 
E6082810.0%
 
R495568.2%
 
T453787.5%
 
O448167.4%
 
N422016.9%
 
I404556.7%
 
S380396.3%
 
C316695.2%
 
L298584.9%
 
M183463.0%
 
D176562.9%
 
P175452.9%
 
H169782.8%
 
U160692.6%
 
G127472.1%
 
F119462.0%
 
V101261.7%
 
Y92171.5%
 
B91471.5%
 
W89291.5%
 
K80451.3%
 
X20110.3%
 
Z12150.2%
 
J9440.2%
 
Other values (25)6760.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
7575380.3%
 
/24962.6%
 
&17451.8%
 
017191.8%
 
-17051.8%
 
.15381.6%
 
110841.1%
 
'10131.1%
 
29931.1%
 
98280.9%
 
78010.8%
 
57600.8%
 
37560.8%
 
47470.8%
 
#5850.6%
 
63780.4%
 
83380.4%
 
(3230.3%
 
)3210.3%
 
,2630.3%
 
@910.1%
 
`47< 0.1%
 
+18< 0.1%
 
:16< 0.1%
 
?8< 0.1%
 
Other values (8)21< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII701787> 99.9%
 
None8< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
7575310.8%
 
A630519.0%
 
E608288.7%
 
R495567.1%
 
T453786.5%
 
O448166.4%
 
N422016.0%
 
I404555.8%
 
S380395.4%
 
C316694.5%
 
L298584.3%
 
M183462.6%
 
D176562.5%
 
P175452.5%
 
H169782.4%
 
U160692.3%
 
G127471.8%
 
F119461.7%
 
V101261.4%
 
Y92171.3%
 
B91471.3%
 
W89291.3%
 
K80451.1%
 
/24960.4%
 
X20110.3%
 
Other values (54)189252.7%
 

Most frequent None characters

ValueCountFrequency (%) 
ó225.0%
 
é225.0%
 
¼225.0%
 
£225.0%
 

STRAP
Categorical

HIGH CARDINALITY
UNIFORM

Distinct512180
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
 
28
153323ZZZ000000000200A
 
1
21292932R000001000020U
 
1
21292932R000002000060U
 
1
21292932R000002000040U
 
1
Other values (512175)
512175 
ValueCountFrequency (%) 
28< 0.1%
 
153323ZZZ000000000200A1< 0.1%
 
21292932R000001000020U1< 0.1%
 
21292932R000002000060U1< 0.1%
 
21292932R000002000040U1< 0.1%
 
21292932R000002000050U1< 0.1%
 
21292932R000002000030U1< 0.1%
 
21292932R000002000020U1< 0.1%
 
21292932R000002000010U1< 0.1%
 
21292932R000001000090U1< 0.1%
 
21292932R000001000080U1< 0.1%
 
21292932R000001000070U1< 0.1%
 
21292932R000001000060U1< 0.1%
 
21292932R000001000050U1< 0.1%
 
21292932R000001000040U1< 0.1%
 
21292932R000001000030U1< 0.1%
 
21292932R000001000010U1< 0.1%
 
21292932R000002000080U1< 0.1%
 
21292932Q000000B00000U1< 0.1%
 
21292932Q000000A00000U1< 0.1%
 
21292932Q000000000490U1< 0.1%
 
21292932Q000000000480U1< 0.1%
 
21292932Q000000000470U1< 0.1%
 
21292932Q000000000460U1< 0.1%
 
21292932Q000000000450U1< 0.1%
 
Other values (512155)512155> 99.9%
 
2022-02-24T09:05:29.178012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique512179 ?
Unique (%)> 99.9%
2022-02-24T09:05:29.280107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length22
Mean length21.99879736
Min length0

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0518780046.0%
 
211063289.8%
 
110461449.3%
 
35752825.1%
 
84758974.2%
 
94625874.1%
 
U3798123.4%
 
73000532.7%
 
42873892.6%
 
52631132.3%
 
62136481.9%
 
A1907101.7%
 
Z1735491.5%
 
B513340.5%
 
P432380.4%
 
C407040.4%
 
T342810.3%
 
D285930.3%
 
E273970.2%
 
J252120.2%
 
X247030.2%
 
V240080.2%
 
F237250.2%
 
I235180.2%
 
Q230550.2%
 
Other values (11)2358582.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number991824188.0%
 
Uppercase Letter134969712.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0518780052.3%
 
2110632811.2%
 
1104614410.5%
 
35752825.8%
 
84758974.8%
 
94625874.7%
 
73000533.0%
 
42873892.9%
 
52631132.7%
 
62136482.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U37981228.1%
 
A19071014.1%
 
Z17354912.9%
 
B513343.8%
 
P432383.2%
 
C407043.0%
 
T342812.5%
 
D285932.1%
 
E273972.0%
 
J252121.9%
 
X247031.8%
 
V240081.8%
 
F237251.8%
 
I235181.7%
 
Q230551.7%
 
W228331.7%
 
H227661.7%
 
S223441.7%
 
R221591.6%
 
Y219851.6%
 
G218831.6%
 
N212791.6%
 
L210061.6%
 
O204531.5%
 
M200811.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common991824188.0%
 
Latin134969712.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0518780052.3%
 
2110632811.2%
 
1104614410.5%
 
35752825.8%
 
84758974.8%
 
94625874.7%
 
73000533.0%
 
42873892.9%
 
52631132.7%
 
62136482.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
U37981228.1%
 
A19071014.1%
 
Z17354912.9%
 
B513343.8%
 
P432383.2%
 
C407043.0%
 
T342812.5%
 
D285932.1%
 
E273972.0%
 
J252121.9%
 
X247031.8%
 
V240081.8%
 
F237251.8%
 
I235181.7%
 
Q230551.7%
 
W228331.7%
 
H227661.7%
 
S223441.7%
 
R221591.6%
 
Y219851.6%
 
G218831.6%
 
N212791.6%
 
L210061.6%
 
O204531.5%
 
M200811.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII11267938100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0518780046.0%
 
211063289.8%
 
110461449.3%
 
35752825.1%
 
84758974.2%
 
94625874.1%
 
U3798123.4%
 
73000532.7%
 
42873892.6%
 
52631132.3%
 
62136481.9%
 
A1907101.7%
 
Z1735491.5%
 
B513340.5%
 
P432380.4%
 
C407040.4%
 
T342810.3%
 
D285930.3%
 
E273970.2%
 
J252120.2%
 
X247030.2%
 
V240080.2%
 
F237250.2%
 
I235180.2%
 
Q230550.2%
 
Other values (11)2358582.1%
 

tBEDS
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.625938537
Minimum0
Maximum338
Zeros81938
Zeros (%)16.0%
Memory size3.9 MiB
2022-02-24T09:05:29.375126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum338
Range338
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.599464514
Coefficient of variation (CV)0.6091020378
Kurtosis4191.105817
Mean2.625938537
Median Absolute Deviation (MAD)1
Skewness22.91204408
Sum1345024.1
Variance2.558286731
MonotocityNot monotonic
2022-02-24T09:05:29.476786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%) 
317643434.4%
 
411204421.9%
 
29703818.9%
 
08193816.0%
 
5250014.9%
 
1146882.9%
 
640360.8%
 
76180.1%
 
8173< 0.1%
 
960< 0.1%
 
1038< 0.1%
 
1228< 0.1%
 
1122< 0.1%
 
3.512< 0.1%
 
1610< 0.1%
 
2.510< 0.1%
 
138< 0.1%
 
207< 0.1%
 
144< 0.1%
 
243< 0.1%
 
0.32< 0.1%
 
172< 0.1%
 
1.52< 0.1%
 
282< 0.1%
 
262< 0.1%
 
Other values (22)25< 0.1%
 
ValueCountFrequency (%) 
08193816.0%
 
0.32< 0.1%
 
1146882.9%
 
1.52< 0.1%
 
29703818.9%
 
2.510< 0.1%
 
317643434.4%
 
3.512< 0.1%
 
411204421.9%
 
5250014.9%
 
ValueCountFrequency (%) 
3381< 0.1%
 
1721< 0.1%
 
1281< 0.1%
 
1101< 0.1%
 
961< 0.1%
 
881< 0.1%
 
721< 0.1%
 
621< 0.1%
 
601< 0.1%
 
521< 0.1%
 

tBATHS
Real number (ℝ≥0)

ZEROS

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.840317684
Minimum0
Maximum170
Zeros81336
Zeros (%)15.9%
Memory size3.9 MiB
2022-02-24T09:05:29.587785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32.5
95-th percentile3.5
Maximum170
Range170
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.148642445
Coefficient of variation (CV)0.6241544356
Kurtosis1120.116116
Mean1.840317684
Median Absolute Deviation (MAD)0.5
Skewness9.721025322
Sum942623.6
Variance1.319379467
MonotocityNot monotonic
2022-02-24T09:05:29.697025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
220743340.5%
 
08133615.9%
 
16507112.7%
 
2.56146212.0%
 
35343610.4%
 
3.5140512.7%
 
1.5111782.2%
 
4102992.0%
 
4.536620.7%
 
519620.4%
 
5.510080.2%
 
65230.1%
 
6.53340.1%
 
7140< 0.1%
 
7.5100< 0.1%
 
863< 0.1%
 
8.533< 0.1%
 
923< 0.1%
 
1012< 0.1%
 
129< 0.1%
 
9.59< 0.1%
 
0.57< 0.1%
 
116< 0.1%
 
10.56< 0.1%
 
145< 0.1%
 
Other values (25)39< 0.1%
 
ValueCountFrequency (%) 
08133615.9%
 
0.57< 0.1%
 
16507112.7%
 
1.11< 0.1%
 
1.5111782.2%
 
220743340.5%
 
2.56146212.0%
 
35343610.4%
 
3.5140512.7%
 
4102992.0%
 
ValueCountFrequency (%) 
1701< 0.1%
 
941< 0.1%
 
861< 0.1%
 
722< 0.1%
 
57.51< 0.1%
 
561< 0.1%
 
451< 0.1%
 
421< 0.1%
 
341< 0.1%
 
301< 0.1%
 

tSTORIES
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct120
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.222605314
Minimum0
Maximum305
Zeros50849
Zeros (%)9.9%
Memory size3.9 MiB
2022-02-24T09:05:29.808652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31.5
95-th percentile2
Maximum305
Range305
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.728493167
Coefficient of variation (CV)1.41377855
Kurtosis5648.781339
Mean1.222605314
Median Absolute Deviation (MAD)0
Skewness54.39155943
Sum626227
Variance2.987688629
MonotocityNot monotonic
2022-02-24T09:05:29.909315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
133251364.9%
 
211642522.7%
 
0508499.9%
 
365731.3%
 
1.521800.4%
 
415650.3%
 
53960.1%
 
6255< 0.1%
 
2.5212< 0.1%
 
7143< 0.1%
 
8116< 0.1%
 
1074< 0.1%
 
973< 0.1%
 
3.566< 0.1%
 
1247< 0.1%
 
1342< 0.1%
 
1140< 0.1%
 
1435< 0.1%
 
1633< 0.1%
 
1732< 0.1%
 
1526< 0.1%
 
2023< 0.1%
 
2123< 0.1%
 
1921< 0.1%
 
4.520< 0.1%
 
Other values (95)4250.1%
 
ValueCountFrequency (%) 
0508499.9%
 
133251364.9%
 
1.521800.4%
 
211642522.7%
 
2.5212< 0.1%
 
365731.3%
 
3.566< 0.1%
 
415650.3%
 
4.520< 0.1%
 
53960.1%
 
ValueCountFrequency (%) 
3051< 0.1%
 
2591< 0.1%
 
2471< 0.1%
 
2141< 0.1%
 
1701< 0.1%
 
1651< 0.1%
 
1631< 0.1%
 
1511< 0.1%
 
1371< 0.1%
 
1351< 0.1%
 

tUNITS
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct269
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.073600127
Minimum0
Maximum1914
Zeros80758
Zeros (%)15.8%
Memory size3.9 MiB
2022-02-24T09:05:30.023170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum1914
Range1914
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.178346894
Coefficient of variation (CV)7.617684361
Kurtosis8093.052348
Mean1.073600127
Median Absolute Deviation (MAD)0
Skewness66.35070617
Sum549905.5
Variance66.88535791
MonotocityNot monotonic
2022-02-24T09:05:30.170737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
142002482.0%
 
08075815.8%
 
287201.7%
 
49100.2%
 
37410.1%
 
598< 0.1%
 
896< 0.1%
 
680< 0.1%
 
1267< 0.1%
 
1046< 0.1%
 
1637< 0.1%
 
729< 0.1%
 
2427< 0.1%
 
1423< 0.1%
 
922< 0.1%
 
1815< 0.1%
 
4011< 0.1%
 
2010< 0.1%
 
3009< 0.1%
 
139< 0.1%
 
289< 0.1%
 
328< 0.1%
 
488< 0.1%
 
117< 0.1%
 
367< 0.1%
 
Other values (244)4360.1%
 
ValueCountFrequency (%) 
08075815.8%
 
142002482.0%
 
287201.7%
 
37410.1%
 
3.51< 0.1%
 
49100.2%
 
598< 0.1%
 
680< 0.1%
 
729< 0.1%
 
896< 0.1%
 
ValueCountFrequency (%) 
19141< 0.1%
 
7761< 0.1%
 
7711< 0.1%
 
7201< 0.1%
 
7121< 0.1%
 
6901< 0.1%
 
6881< 0.1%
 
6381< 0.1%
 
5971< 0.1%
 
5401< 0.1%
 

tBLDGS
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct69
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8047156716
Minimum0
Maximum174
Zeros127229
Zeros (%)24.8%
Memory size3.9 MiB
2022-02-24T09:05:30.330764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum174
Range174
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.013424464
Coefficient of variation (CV)1.259357186
Kurtosis6960.77856
Mean0.8047156716
Median Absolute Deviation (MAD)0
Skewness57.03203213
Sum412181
Variance1.027029144
MonotocityNot monotonic
2022-02-24T09:05:30.476591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
137269872.8%
 
012722924.8%
 
292721.8%
 
312880.3%
 
45390.1%
 
52700.1%
 
6152< 0.1%
 
7108< 0.1%
 
884< 0.1%
 
1065< 0.1%
 
964< 0.1%
 
1349< 0.1%
 
1245< 0.1%
 
1134< 0.1%
 
1432< 0.1%
 
1630< 0.1%
 
1827< 0.1%
 
1522< 0.1%
 
1722< 0.1%
 
2115< 0.1%
 
1914< 0.1%
 
2410< 0.1%
 
2210< 0.1%
 
209< 0.1%
 
259< 0.1%
 
Other values (44)110< 0.1%
 
ValueCountFrequency (%) 
012722924.8%
 
137269872.8%
 
292721.8%
 
312880.3%
 
45390.1%
 
52700.1%
 
6152< 0.1%
 
7108< 0.1%
 
884< 0.1%
 
964< 0.1%
 
ValueCountFrequency (%) 
1741< 0.1%
 
1691< 0.1%
 
1561< 0.1%
 
1551< 0.1%
 
1401< 0.1%
 
871< 0.1%
 
861< 0.1%
 
801< 0.1%
 
781< 0.1%
 
771< 0.1%
 

TAXDIST
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
U
353271 
TA
136169 
PC
 
14075
TT
 
8664
 
28
ValueCountFrequency (%) 
U35327169.0%
 
TA13616926.6%
 
PC140752.7%
 
TT86641.7%
 
28< 0.1%
 
2022-02-24T09:05:30.599438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T09:05:30.666215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:05:30.745638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.310187092
Min length0

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
U35327152.6%
 
T15349722.9%
 
A13616920.3%
 
P140752.1%
 
C140752.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter671087100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U35327152.6%
 
T15349722.9%
 
A13616920.3%
 
P140752.1%
 
C140752.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin671087100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
U35327152.6%
 
T15349722.9%
 
A13616920.3%
 
P140752.1%
 
C140752.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII671087100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
U35327152.6%
 
T15349722.9%
 
A13616920.3%
 
P140752.1%
 
C140752.1%
 

JUST
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct292394
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean373126.5019
Minimum0
Maximum807103929
Zeros834
Zeros (%)0.2%
Memory size3.9 MiB
2022-02-24T09:05:30.953141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25173
Q1149765
median231370
Q3330901
95-th percentile718441.1
Maximum807103929
Range807103929
Interquartile range (IQR)181136

Descriptive statistics

Standard deviation2298911.234
Coefficient of variation (CV)6.161211338
Kurtosis34045.10721
Mean373126.5019
Median Absolute Deviation (MAD)89542
Skewness126.3349262
Sum1.911180061e+11
Variance5.28499286e+12
MonotocityNot monotonic
2022-02-24T09:05:31.059858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100101592.0%
 
08340.2%
 
60004780.1%
 
4800248< 0.1%
 
10000216< 0.1%
 
103539215< 0.1%
 
12000211< 0.1%
 
74374188< 0.1%
 
25254180< 0.1%
 
7200179< 0.1%
 
56227177< 0.1%
 
153411171< 0.1%
 
66309167< 0.1%
 
20000163< 0.1%
 
27000161< 0.1%
 
4692160< 0.1%
 
101213155< 0.1%
 
5867152< 0.1%
 
71960151< 0.1%
 
81247139< 0.1%
 
30000138< 0.1%
 
55676137< 0.1%
 
118750134< 0.1%
 
60777134< 0.1%
 
161358128< 0.1%
 
Other values (292369)49703297.0%
 
ValueCountFrequency (%) 
08340.2%
 
516< 0.1%
 
71< 0.1%
 
810< 0.1%
 
1022< 0.1%
 
121< 0.1%
 
141< 0.1%
 
1540< 0.1%
 
161< 0.1%
 
181< 0.1%
 
ValueCountFrequency (%) 
8071039291< 0.1%
 
4116012001< 0.1%
 
3296113581< 0.1%
 
2739799911< 0.1%
 
2490912141< 0.1%
 
2398016581< 0.1%
 
1960676001< 0.1%
 
1796665461< 0.1%
 
1630796001< 0.1%
 
1540685581< 0.1%
 

LAND
Real number (ℝ≥0)

SKEWED

Distinct124793
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108788.6712
Minimum0
Maximum101966730
Zeros837
Zeros (%)0.2%
Memory size3.9 MiB
2022-02-24T09:05:31.217114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100
Q137824
median63315
Q395056.5
95-th percentile282777.4
Maximum101966730
Range101966730
Interquartile range (IQR)57232.5

Descriptive statistics

Standard deviation446963.5074
Coefficient of variation (CV)4.108548275
Kurtosis13128.50165
Mean108788.6712
Median Absolute Deviation (MAD)28554
Skewness78.73048924
Sum5.572231891e+10
Variance1.997763769e+11
MonotocityNot monotonic
2022-02-24T09:05:31.323616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1005604010.9%
 
8000010710.2%
 
504909420.2%
 
08370.2%
 
594007370.1%
 
393907290.1%
 
630006830.1%
 
325006810.1%
 
2100006180.1%
 
1248005860.1%
 
586505720.1%
 
415805590.1%
 
60005420.1%
 
673205380.1%
 
415005310.1%
 
463505300.1%
 
643374820.1%
 
450004720.1%
 
550004680.1%
 
2000004550.1%
 
610004330.1%
 
550804320.1%
 
676204140.1%
 
403754130.1%
 
611054110.1%
 
Other values (124768)44203186.3%
 
ValueCountFrequency (%) 
08370.2%
 
516< 0.1%
 
71< 0.1%
 
812< 0.1%
 
1026< 0.1%
 
121< 0.1%
 
141< 0.1%
 
1541< 0.1%
 
161< 0.1%
 
181< 0.1%
 
ValueCountFrequency (%) 
1019667301< 0.1%
 
954918631< 0.1%
 
820080491< 0.1%
 
549025101< 0.1%
 
433122141< 0.1%
 
402228701< 0.1%
 
399584041< 0.1%
 
377641221< 0.1%
 
370528261< 0.1%
 
340391341< 0.1%
 

BLDG
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct231462
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252989.2721
Minimum0
Maximum569648815
Zeros54807
Zeros (%)10.7%
Memory size3.9 MiB
2022-02-24T09:05:31.488697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q199059
median158989
Q3225782
95-th percentile453101.1
Maximum569648815
Range569648815
Interquartile range (IQR)126723

Descriptive statistics

Standard deviation1802786.325
Coefficient of variation (CV)7.125939807
Kurtosis23463.80968
Mean252989.2721
Median Absolute Deviation (MAD)63167
Skewness104.9611111
Sum1.295828761e+11
Variance3.250038534e+12
MonotocityNot monotonic
2022-02-24T09:05:31.595618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05480710.7%
 
103439217< 0.1%
 
74274188< 0.1%
 
25154181< 0.1%
 
56127178< 0.1%
 
153311173< 0.1%
 
66209167< 0.1%
 
101113155< 0.1%
 
71860153< 0.1%
 
178896142< 0.1%
 
118650137< 0.1%
 
55576136< 0.1%
 
60677135< 0.1%
 
81147133< 0.1%
 
161258132< 0.1%
 
165771125< 0.1%
 
28311123< 0.1%
 
36215118< 0.1%
 
131773117< 0.1%
 
157482117< 0.1%
 
6443114< 0.1%
 
30126107< 0.1%
 
73393107< 0.1%
 
111064104< 0.1%
 
89009104< 0.1%
 
Other values (231437)45403788.6%
 
ValueCountFrequency (%) 
05480710.7%
 
1001< 0.1%
 
1311< 0.1%
 
4021< 0.1%
 
5161< 0.1%
 
5231< 0.1%
 
5411< 0.1%
 
5771< 0.1%
 
6181< 0.1%
 
6331< 0.1%
 
ValueCountFrequency (%) 
5696488151< 0.1%
 
2900579441< 0.1%
 
2385348921< 0.1%
 
2292132271< 0.1%
 
2223842601< 0.1%
 
1530146711< 0.1%
 
1476524411< 0.1%
 
1469310171< 0.1%
 
1425639441< 0.1%
 
1295114191< 0.1%
 

EXF
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct47166
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12266.92599
Minimum0
Maximum155447065
Zeros231678
Zeros (%)45.2%
Memory size3.9 MiB
2022-02-24T09:05:31.725918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median609
Q38334
95-th percentile37145
Maximum155447065
Range155447065
Interquartile range (IQR)8334

Descriptive statistics

Standard deviation331912.9874
Coefficient of variation (CV)27.05755196
Kurtosis168986.0702
Mean12266.92599
Median Absolute Deviation (MAD)609
Skewness378.5101548
Sum6283205361
Variance1.101662312e+11
MonotocityNot monotonic
2022-02-24T09:05:31.847526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
023167845.2%
 
250255181.1%
 
950016340.3%
 
262714970.3%
 
275213630.3%
 
200212570.2%
 
300211940.2%
 
1335011560.2%
 
2531210510.2%
 
246719190.2%
 
76007660.1%
 
158527560.1%
 
31287540.1%
 
313996550.1%
 
237106230.1%
 
120026210.1%
 
28776140.1%
 
160206030.1%
 
210935670.1%
 
225275620.1%
 
190225520.1%
 
205595520.1%
 
256325400.1%
 
197585390.1%
 
33035200.1%
 
Other values (47141)25571649.9%
 
ValueCountFrequency (%) 
023167845.2%
 
51< 0.1%
 
61< 0.1%
 
71< 0.1%
 
91< 0.1%
 
152< 0.1%
 
161< 0.1%
 
181< 0.1%
 
191< 0.1%
 
341< 0.1%
 
ValueCountFrequency (%) 
1554470651< 0.1%
 
1466713941< 0.1%
 
331952781< 0.1%
 
295397761< 0.1%
 
271446961< 0.1%
 
262562351< 0.1%
 
224319011< 0.1%
 
190915871< 0.1%
 
185348741< 0.1%
 
177425321< 0.1%
 

ACT
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct140
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1789.886118
Minimum0
Maximum2021
Zeros50718
Zeros (%)9.9%
Memory size3.9 MiB
2022-02-24T09:05:31.960577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11962
median1985
Q32003
95-th percentile2018
Maximum2021
Range2021
Interquartile range (IQR)41

Descriptive statistics

Standard deviation593.7733545
Coefficient of variation (CV)0.3317380634
Kurtosis5.187692516
Mean1789.886118
Median Absolute Deviation (MAD)19
Skewness-2.678203648
Sum916792199
Variance352566.7965
MonotocityNot monotonic
2022-02-24T09:05:32.061988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0507189.9%
 
2005130352.5%
 
2006129012.5%
 
2001123532.4%
 
2004110032.1%
 
2003109692.1%
 
1986104932.0%
 
198497611.9%
 
200794451.8%
 
200091121.8%
 
198590711.8%
 
200289841.8%
 
202088891.7%
 
201988551.7%
 
198387481.7%
 
199986471.7%
 
197982861.6%
 
198179831.6%
 
201879721.6%
 
199876781.5%
 
198775541.5%
 
198874761.5%
 
198073331.4%
 
202172191.4%
 
201769361.4%
 
Other values (115)24078647.0%
 
ValueCountFrequency (%) 
0507189.9%
 
18601< 0.1%
 
18721< 0.1%
 
18751< 0.1%
 
18803< 0.1%
 
18821< 0.1%
 
18851< 0.1%
 
18862< 0.1%
 
18872< 0.1%
 
18881< 0.1%
 
ValueCountFrequency (%) 
202172191.4%
 
202088891.7%
 
201988551.7%
 
201879721.6%
 
201769361.4%
 
201659301.2%
 
201551911.0%
 
201445630.9%
 
201348580.9%
 
201236270.7%
 

EFF
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct115
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1805.075194
Minimum0
Maximum2021
Zeros50718
Zeros (%)9.9%
Memory size3.9 MiB
2022-02-24T09:05:32.162208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11991
median2003
Q32011
95-th percentile2019
Maximum2021
Range2021
Interquartile range (IQR)20

Descriptive statistics

Standard deviation598.5115688
Coefficient of variation (CV)0.3315715438
Kurtosis5.203525471
Mean1805.075194
Median Absolute Deviation (MAD)10
Skewness-2.68320887
Sum924572150
Variance358216.0979
MonotocityNot monotonic
2022-02-24T09:05:32.253325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0507189.9%
 
2013344646.7%
 
2011308606.0%
 
2003253104.9%
 
2019232464.5%
 
2001231574.5%
 
2009215464.2%
 
1999183563.6%
 
2007167753.3%
 
2005150142.9%
 
2017145092.8%
 
1997139482.7%
 
1989129302.5%
 
2002117952.3%
 
2012116892.3%
 
1991112282.2%
 
2010105232.1%
 
2015104332.0%
 
1995101642.0%
 
202089871.8%
 
200088111.7%
 
200483071.6%
 
198781741.6%
 
200879471.6%
 
199377711.5%
 
Other values (90)9554518.7%
 
ValueCountFrequency (%) 
0507189.9%
 
18861< 0.1%
 
19007< 0.1%
 
19041< 0.1%
 
19052< 0.1%
 
19061< 0.1%
 
19084< 0.1%
 
19104< 0.1%
 
19122< 0.1%
 
19134< 0.1%
 
ValueCountFrequency (%) 
202172221.4%
 
202089871.8%
 
2019232464.5%
 
201860801.2%
 
2017145092.8%
 
201642670.8%
 
2015104332.0%
 
201459411.2%
 
2013344646.7%
 
2012116892.3%
 

HEAT_AR
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct13642
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2613.202285
Minimum0
Maximum4011409
Zeros50759
Zeros (%)9.9%
Memory size3.9 MiB
2022-02-24T09:05:32.349800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11152
median1600
Q32227
95-th percentile3796
Maximum4011409
Range4011409
Interquartile range (IQR)1075

Descriptive statistics

Standard deviation16203.22615
Coefficient of variation (CV)6.200525019
Kurtosis10705.82671
Mean2613.202285
Median Absolute Deviation (MAD)512
Skewness68.24408643
Sum1338500503
Variance262544537.7
MonotocityNot monotonic
2022-02-24T09:05:32.438829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0507599.9%
 
120018410.4%
 
96016560.3%
 
115214820.3%
 
129612600.2%
 
124812100.2%
 
134411750.2%
 
151611330.2%
 
105611160.2%
 
144011110.2%
 
140410720.2%
 
193510450.2%
 
191410330.2%
 
86410240.2%
 
11769660.2%
 
10809630.2%
 
15129570.2%
 
9129560.2%
 
12609500.2%
 
14009280.2%
 
6728850.2%
 
10088690.2%
 
8408380.2%
 
9248280.2%
 
11848270.2%
 
Other values (13617)43532385.0%
 
ValueCountFrequency (%) 
0507599.9%
 
641< 0.1%
 
651< 0.1%
 
771< 0.1%
 
801< 0.1%
 
961< 0.1%
 
1102< 0.1%
 
1121< 0.1%
 
1161< 0.1%
 
1285< 0.1%
 
ValueCountFrequency (%) 
40114091< 0.1%
 
26121241< 0.1%
 
22205311< 0.1%
 
18006981< 0.1%
 
17897931< 0.1%
 
12782171< 0.1%
 
12367181< 0.1%
 
11686821< 0.1%
 
11073771< 0.1%
 
10836661< 0.1%
 

ASD_VAL
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct274008
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287120.5446
Minimum0
Maximum807103929
Zeros832
Zeros (%)0.2%
Memory size3.9 MiB
2022-02-24T09:05:32.602461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10732.5
Q182731
median151219
Q3236575
95-th percentile547972.8
Maximum807103929
Range807103929
Interquartile range (IQR)153844

Descriptive statistics

Standard deviation2257633.735
Coefficient of variation (CV)7.86301704
Kurtosis36410.47334
Mean287120.5446
Median Absolute Deviation (MAD)74753
Skewness131.3984257
Sum1.470651528e+11
Variance5.09691008e+12
MonotocityNot monotonic
2022-02-24T09:05:32.688179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100101332.0%
 
08320.2%
 
60004900.1%
 
4800249< 0.1%
 
92485195< 0.1%
 
12000187< 0.1%
 
7200179< 0.1%
 
10000175< 0.1%
 
4692160< 0.1%
 
5867152< 0.1%
 
20000147< 0.1%
 
30000137< 0.1%
 
27000137< 0.1%
 
98799117< 0.1%
 
49489115< 0.1%
 
6543114< 0.1%
 
500105< 0.1%
 
32265104< 0.1%
 
117809101< 0.1%
 
3022694< 0.1%
 
2366292< 0.1%
 
8124790< 0.1%
 
2074889< 0.1%
 
6121386< 0.1%
 
7437485< 0.1%
 
Other values (273983)49784297.2%
 
ValueCountFrequency (%) 
08320.2%
 
110< 0.1%
 
23< 0.1%
 
31< 0.1%
 
42< 0.1%
 
517< 0.1%
 
61< 0.1%
 
71< 0.1%
 
811< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
8071039291< 0.1%
 
4116012001< 0.1%
 
3216252661< 0.1%
 
2737368551< 0.1%
 
2397890141< 0.1%
 
2335577131< 0.1%
 
1960676001< 0.1%
 
1759843051< 0.1%
 
1562137031< 0.1%
 
1423177721< 0.1%
 

TAX_VAL
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct252545
Distinct (%)49.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227157.8501
Minimum0
Maximum411601200
Zeros29772
Zeros (%)5.8%
Memory size3.9 MiB
2022-02-24T09:05:33.247630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q144151.5
median113049
Q3202127
95-th percentile485496.8
Maximum411601200
Range411601200
Interquartile range (IQR)157975.5

Descriptive statistics

Standard deviation1609346.739
Coefficient of variation (CV)7.084706686
Kurtosis10269.40316
Mean227157.8501
Median Absolute Deviation (MAD)76569
Skewness66.72198269
Sum1.163518409e+11
Variance2.589996927e+12
MonotocityNot monotonic
2022-02-24T09:05:33.321564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0297725.8%
 
25000203254.0%
 
10073941.4%
 
2450021510.4%
 
60004910.1%
 
200004830.1%
 
4800248< 0.1%
 
92485195< 0.1%
 
12000190< 0.1%
 
7200179< 0.1%
 
10000175< 0.1%
 
4692161< 0.1%
 
5867153< 0.1%
 
27000136< 0.1%
 
30000124< 0.1%
 
98799118< 0.1%
 
49489116< 0.1%
 
117809102< 0.1%
 
654398< 0.1%
 
3022695< 0.1%
 
2400092< 0.1%
 
2366292< 0.1%
 
8124789< 0.1%
 
6121388< 0.1%
 
11140585< 0.1%
 
Other values (252520)44905587.7%
 
ValueCountFrequency (%) 
0297725.8%
 
18< 0.1%
 
23< 0.1%
 
31< 0.1%
 
41< 0.1%
 
58< 0.1%
 
61< 0.1%
 
86< 0.1%
 
91< 0.1%
 
1012< 0.1%
 
ValueCountFrequency (%) 
4116012001< 0.1%
 
1960676001< 0.1%
 
1562137031< 0.1%
 
1396106001< 0.1%
 
1233091001< 0.1%
 
1195872001< 0.1%
 
1123096001< 0.1%
 
1121445001< 0.1%
 
1100819001< 0.1%
 
1087548001< 0.1%
 

MUNI
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
U
353271 
A
136169 
P
 
14075
T
 
8664
 
28
ValueCountFrequency (%) 
U35327169.0%
 
A13616926.6%
 
P140752.7%
 
T86641.7%
 
28< 0.1%
 
2022-02-24T09:05:33.397269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T09:05:33.445044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:05:33.495776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length0.9999453346
Min length0

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
U35327169.0%
 
A13616926.6%
 
P140752.7%
 
T86641.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter512179100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U35327169.0%
 
A13616926.6%
 
P140752.7%
 
T86641.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin512179100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
U35327169.0%
 
A13616926.6%
 
P140752.7%
 
T86641.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII512179100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
U35327169.0%
 
A13616926.6%
 
P140752.7%
 
T86641.7%
 

SD1
Categorical

HIGH CARDINALITY

Distinct189
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
391945 
702
 
8488
154
 
5339
043
 
5118
006
 
4881
Other values (184)
96436 
ValueCountFrequency (%) 
39194576.5%
 
70284881.7%
 
15453391.0%
 
04351181.0%
 
00648811.0%
 
03738370.7%
 
04735420.7%
 
00527600.5%
 
04126990.5%
 
01222820.4%
 
09720770.4%
 
07719000.4%
 
03418670.4%
 
11917110.3%
 
06216680.3%
 
01116260.3%
 
11615570.3%
 
06314540.3%
 
11813910.3%
 
05313140.3%
 
YGR13060.3%
 
12412800.2%
 
14012730.2%
 
06112660.2%
 
08812120.2%
 
Other values (164)5841411.4%
 
2022-02-24T09:05:33.579496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)< 0.1%
2022-02-24T09:05:33.661560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length0
Mean length0.7043753795
Min length0

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
010587629.3%
 
16190517.2%
 
4342889.5%
 
7317258.8%
 
2247316.9%
 
3244436.8%
 
5241686.7%
 
6209855.8%
 
9177404.9%
 
8110073.1%
 
Y13060.4%
 
G13060.4%
 
R13060.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number35686898.9%
 
Uppercase Letter39181.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y130633.3%
 
G130633.3%
 
R130633.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
010587629.7%
 
16190517.3%
 
4342889.6%
 
7317258.9%
 
2247316.9%
 
3244436.8%
 
5241686.8%
 
6209855.9%
 
9177405.0%
 
8110073.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common35686898.9%
 
Latin39181.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Y130633.3%
 
G130633.3%
 
R130633.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
010587629.7%
 
16190517.3%
 
4342889.6%
 
7317258.9%
 
2247316.9%
 
3244436.8%
 
5241686.8%
 
6209855.9%
 
9177405.0%
 
8110073.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII360786100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
010587629.3%
 
16190517.2%
 
4342889.5%
 
7317258.8%
 
2247316.9%
 
3244436.8%
 
5241686.7%
 
6209855.8%
 
9177404.9%
 
8110073.1%
 
Y13060.4%
 
G13060.4%
 
R13060.4%
 

SD2
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
509168 
201
 
2434
YGR
 
332
928
 
127
929
 
90
Other values (7)
 
56
ValueCountFrequency (%) 
50916899.4%
 
20124340.5%
 
YGR3320.1%
 
928127< 0.1%
 
92990< 0.1%
 
70247< 0.1%
 
0414< 0.1%
 
0071< 0.1%
 
9401< 0.1%
 
1411< 0.1%
 
1401< 0.1%
 
1111< 0.1%
 
2022-02-24T09:05:33.730331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)< 0.1%
2022-02-24T09:05:33.797107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length0
Mean length0.01779944437
Min length0

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
2269829.6%
 
0248927.3%
 
1244426.8%
 
Y3323.6%
 
G3323.6%
 
R3323.6%
 
93083.4%
 
81271.4%
 
7480.5%
 
470.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number812189.1%
 
Uppercase Letter99610.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y33233.3%
 
G33233.3%
 
R33233.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
2269833.2%
 
0248930.6%
 
1244430.1%
 
93083.8%
 
81271.6%
 
7480.6%
 
470.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common812189.1%
 
Latin99610.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Y33233.3%
 
G33233.3%
 
R33233.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
2269833.2%
 
0248930.6%
 
1244430.1%
 
93083.8%
 
81271.6%
 
7480.6%
 
470.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9117100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
2269829.6%
 
0248927.3%
 
1244426.8%
 
Y3323.6%
 
G3323.6%
 
R3323.6%
 
93083.4%
 
81271.4%
 
7480.5%
 
470.1%
 

TIF
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
466465 
E
 
17300
9
 
13283
D
 
2686
1
 
2685
Other values (11)
 
9788
ValueCountFrequency (%) 
46646591.1%
 
E173003.4%
 
9132832.6%
 
D26860.5%
 
126850.5%
 
C25750.5%
 
321790.4%
 
612240.2%
 
59360.2%
 
88400.2%
 
27020.1%
 
46810.1%
 
A237< 0.1%
 
N196< 0.1%
 
B170< 0.1%
 
748< 0.1%
 
2022-02-24T09:05:33.869007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T09:05:33.934905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length0
Mean length0.08930373853
Min length0

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
E1730037.8%
 
91328329.0%
 
D26865.9%
 
126855.9%
 
C25755.6%
 
321794.8%
 
612242.7%
 
59362.0%
 
88401.8%
 
27021.5%
 
46811.5%
 
A2370.5%
 
N1960.4%
 
B1700.4%
 
7480.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter2316450.6%
 
Decimal Number2257849.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E1730074.7%
 
D268611.6%
 
C257511.1%
 
A2371.0%
 
N1960.8%
 
B1700.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
91328358.8%
 
1268511.9%
 
321799.7%
 
612245.4%
 
59364.1%
 
88403.7%
 
27023.1%
 
46813.0%
 
7480.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2316450.6%
 
Common2257849.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E1730074.7%
 
D268611.6%
 
C257511.1%
 
A2371.0%
 
N1960.8%
 
B1700.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
91328358.8%
 
1268511.9%
 
321799.7%
 
612245.4%
 
59364.1%
 
88403.7%
 
27023.1%
 
46813.0%
 
7480.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII45742100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
E1730037.8%
 
91328329.0%
 
D26865.9%
 
126855.9%
 
C25755.6%
 
321794.8%
 
612242.7%
 
59362.0%
 
88401.8%
 
27021.5%
 
46811.5%
 
A2370.5%
 
N1960.4%
 
B1700.4%
 
7480.1%
 

BASE
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1145.812463
Minimum0
Maximum2022
Zeros220211
Zeros (%)43.0%
Memory size3.9 MiB
2022-02-24T09:05:34.004653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1994
Q32015
95-th percentile2021
Maximum2022
Range2022
Interquartile range (IQR)2015

Descriptive statistics

Standard deviation995.0766257
Coefficient of variation (CV)0.8684463281
Kurtosis-1.919742165
Mean1145.812463
Median Absolute Deviation (MAD)26
Skewness-0.2829313275
Sum586893164
Variance990177.4911
MonotocityNot monotonic
2022-02-24T09:05:34.080773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
022021143.0%
 
1994394227.7%
 
2021256175.0%
 
2020241994.7%
 
2019213434.2%
 
2018190903.7%
 
2017164713.2%
 
2016138742.7%
 
2015102222.0%
 
201486701.7%
 
200678221.5%
 
200575921.5%
 
200471491.4%
 
201368941.3%
 
200366651.3%
 
200266151.3%
 
200765051.3%
 
201260551.2%
 
200158081.1%
 
200856681.1%
 
201155521.1%
 
200055391.1%
 
201054131.1%
 
199950801.0%
 
200948340.9%
 
Other values (5)198973.9%
 
ValueCountFrequency (%) 
022021143.0%
 
1994394227.7%
 
199537540.7%
 
199638650.8%
 
199740590.8%
 
199843250.8%
 
199950801.0%
 
200055391.1%
 
200158081.1%
 
200266151.3%
 
ValueCountFrequency (%) 
202238940.8%
 
2021256175.0%
 
2020241994.7%
 
2019213434.2%
 
2018190903.7%
 
2017164713.2%
 
2016138742.7%
 
2015102222.0%
 
201486701.7%
 
201368941.3%
 

S_DATE
Categorical

HIGH CARDINALITY
MISSING

Distinct7738
Distinct (%)1.8%
Missing87780
Missing (%)17.1%
Memory size3.9 MiB
1976-01-01
 
2142
1973-01-01
 
2083
1974-01-01
 
1967
1972-01-01
 
1783
1975-01-01
 
1662
Other values (7733)
414790 
ValueCountFrequency (%) 
1976-01-0121420.4%
 
1973-01-0120830.4%
 
1974-01-0119670.4%
 
1972-01-0117830.3%
 
1975-01-0116620.3%
 
1971-01-0114890.3%
 
1970-01-0112450.2%
 
1969-01-017760.2%
 
1998-12-017150.1%
 
1999-06-016990.1%
 
1998-06-016650.1%
 
1999-03-016640.1%
 
1998-07-016520.1%
 
1991-10-016440.1%
 
1999-07-016210.1%
 
1997-12-016150.1%
 
1999-08-015860.1%
 
1998-10-015850.1%
 
1999-04-015570.1%
 
1998-08-015560.1%
 
1999-05-015550.1%
 
1998-04-015540.1%
 
1998-05-015540.1%
 
1994-12-015380.1%
 
1998-03-015350.1%
 
Other values (7713)40098578.3%
 
(Missing)8778017.1%
 
2022-02-24T09:05:34.195013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1034 ?
Unique (%)0.2%
2022-02-24T09:05:34.283463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length8.80036782
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0104125223.1%
 
-84885418.8%
 
173059416.2%
 
263228114.0%
 
92618555.8%
 
n1755603.9%
 
81433093.2%
 
71313962.9%
 
31305402.9%
 
61140182.5%
 
51122132.5%
 
4979582.2%
 
a877801.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number339541675.3%
 
Dash Punctuation84885418.8%
 
Lowercase Letter2633405.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n17556066.7%
 
a8778033.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0104125230.7%
 
173059421.5%
 
263228118.6%
 
92618557.7%
 
81433094.2%
 
71313963.9%
 
31305403.8%
 
61140183.4%
 
51122133.3%
 
4979582.9%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-848854100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common424427094.2%
 
Latin2633405.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n17556066.7%
 
a8778033.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
0104125224.5%
 
-84885420.0%
 
173059417.2%
 
263228114.9%
 
92618556.2%
 
81433093.4%
 
71313963.1%
 
31305403.1%
 
61140182.7%
 
51122132.6%
 
4979582.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4507610100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0104125223.1%
 
-84885418.8%
 
173059416.2%
 
263228114.0%
 
92618555.8%
 
n1755603.9%
 
81433093.2%
 
71313962.9%
 
31305402.9%
 
61140182.5%
 
51122132.5%
 
4979582.2%
 
a877801.9%
 

VI
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
I
385692 
102700 
V
 
23815
ValueCountFrequency (%) 
I38569275.3%
 
10270020.1%
 
V238154.6%
 
2022-02-24T09:05:34.371690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T09:05:34.427150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:05:34.481562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length0.799495126
Min length0

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
I38569294.2%
 
V238155.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter409507100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I38569294.2%
 
V238155.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin409507100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
I38569294.2%
 
V238155.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII409507100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
I38569294.2%
 
V238155.8%
 

S_AMT
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct10981
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222262.8388
Minimum0
Maximum350005000
Zeros87780
Zeros (%)17.1%
Memory size3.9 MiB
2022-02-24T09:05:34.567031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q138700
median137000
Q3248000
95-th percentile515000
Maximum350005000
Range350005000
Interquartile range (IQR)209300

Descriptive statistics

Standard deviation1411635.647
Coefficient of variation (CV)6.351199572
Kurtosis10283.42417
Mean222262.8388
Median Absolute Deviation (MAD)103200
Skewness73.61751948
Sum1.138445819e+11
Variance1.992715199e+12
MonotocityNot monotonic
2022-02-24T09:05:34.655863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
08778017.1%
 
15000028470.6%
 
20000025760.5%
 
12500025420.5%
 
17500025330.5%
 
25000024840.5%
 
22500023140.5%
 
16500023030.4%
 
16000022550.4%
 
13500022270.4%
 
14000022210.4%
 
30000021880.4%
 
12000021750.4%
 
18000021640.4%
 
13000021570.4%
 
7500021510.4%
 
18500021030.4%
 
17000020890.4%
 
22000020720.4%
 
21000020590.4%
 
6500020450.4%
 
21500020450.4%
 
11000020420.4%
 
15500020350.4%
 
10000020170.4%
 
Other values (10956)37078372.4%
 
ValueCountFrequency (%) 
08778017.1%
 
11< 0.1%
 
1011< 0.1%
 
1008< 0.1%
 
1011< 0.1%
 
1251< 0.1%
 
1461< 0.1%
 
1505< 0.1%
 
1601< 0.1%
 
1801< 0.1%
 
ValueCountFrequency (%) 
3500050001< 0.1%
 
1570000001< 0.1%
 
1457500001< 0.1%
 
1447780001< 0.1%
 
1360000001< 0.1%
 
1315000001< 0.1%
 
1250000001< 0.1%
 
1200000001< 0.1%
 
1124250001< 0.1%
 
1122500001< 0.1%
 

ACREAGE
Real number (ℝ≥0)

SKEWED

Distinct298441
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.533731901
Minimum2.03714e-05
Maximum50345.2
Zeros0
Zeros (%)0.0%
Memory size3.9 MiB
2022-02-24T09:05:34.827701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.03714e-05
5-th percentile0.0101712
Q10.120523
median0.178638
Q30.3131795
95-th percentile2.464791
Maximum50345.2
Range50345.19998
Interquartile range (IQR)0.1926565

Descriptive statistics

Standard deviation86.26654734
Coefficient of variation (CV)56.24617135
Kurtosis237378.0197
Mean1.533731901
Median Absolute Deviation (MAD)0.076508
Skewness441.4208287
Sum785588.2161
Variance7441.91719
MonotocityNot monotonic
2022-02-24T09:05:34.929345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5.16527e-0579671.6%
 
9.1827e-0551381.0%
 
5.1664e-0522130.4%
 
0.12626317490.3%
 
0.13774117070.3%
 
5.16414e-0516800.3%
 
0.1010110620.2%
 
0.12626210040.2%
 
0.1147847120.1%
 
0.1262645820.1%
 
0.1101935620.1%
 
0.1320025130.1%
 
0.1652895010.1%
 
0.137744910.1%
 
0.1515154800.1%
 
0.1388894560.1%
 
0.1377424530.1%
 
0.1010113990.1%
 
0.1010093580.1%
 
0.1147833240.1%
 
0.1262653230.1%
 
0.1262613130.1%
 
0.1262662920.1%
 
0.1262672710.1%
 
0.1147852580.1%
 
Other values (298416)48239994.2%
 
ValueCountFrequency (%) 
2.03714e-051< 0.1%
 
3.52127e-051< 0.1%
 
3.80899e-051< 0.1%
 
4.20554e-051< 0.1%
 
4.4168e-051< 0.1%
 
4.72805e-051< 0.1%
 
4.78615e-051< 0.1%
 
4.81448e-051< 0.1%
 
4.84166e-051< 0.1%
 
4.89977e-051< 0.1%
 
ValueCountFrequency (%) 
50345.21< 0.1%
 
217301< 0.1%
 
15190.61< 0.1%
 
11075.81< 0.1%
 
9893.131< 0.1%
 
8282.681< 0.1%
 
6901.821< 0.1%
 
5657.261< 0.1%
 
4891.561< 0.1%
 
4381.581< 0.1%
 

NBHC
Real number (ℝ≥0)

Distinct315
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216170.257
Minimum0
Maximum229008
Zeros28
Zeros (%)< 0.1%
Memory size3.9 MiB
2022-02-24T09:05:35.028774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile203001
Q1209008
median217002
Q3223009
95-th percentile228003
Maximum229008
Range229008
Interquartile range (IQR)14001

Descriptive statistics

Standard deviation8465.772439
Coefficient of variation (CV)0.03916252198
Kurtosis21.90378847
Mean216170.257
Median Absolute Deviation (MAD)6999
Skewness-1.126480224
Sum1.107239188e+11
Variance71669303
MonotocityNot monotonic
2022-02-24T09:05:35.129552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
22700193491.8%
 
22800384241.6%
 
22500162891.2%
 
22400559921.2%
 
22600159571.2%
 
22100159201.2%
 
22800457041.1%
 
22600352831.0%
 
21900351051.0%
 
22200650351.0%
 
21200347050.9%
 
22200146150.9%
 
22000344420.9%
 
20500344080.9%
 
22200241770.8%
 
20500641050.8%
 
22600240800.8%
 
20200140150.8%
 
22700337620.7%
 
21200636830.7%
 
21200436540.7%
 
22200435620.7%
 
21400635570.7%
 
22600435540.7%
 
22300834880.7%
 
Other values (290)38934276.0%
 
ValueCountFrequency (%) 
028< 0.1%
 
20100123980.5%
 
20100224780.5%
 
20100321870.4%
 
20100416530.3%
 
20100517830.3%
 
20100610140.2%
 
2010079010.2%
 
2010081< 0.1%
 
20200140150.8%
 
ValueCountFrequency (%) 
22900822710.4%
 
229007141< 0.1%
 
22900622520.4%
 
229004149< 0.1%
 
22900321570.4%
 
2290028920.2%
 
22900118570.4%
 
22800529800.6%
 
22800457041.1%
 
22800384241.6%
 

Interactions

2022-02-24T09:04:11.076836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:11.302930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:11.462691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:11.621459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:11.778935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:11.940507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:12.103949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:12.264616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:12.420176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:12.586893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:12.749532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:12.914581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:13.081641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:13.247338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:13.403435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:13.567417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:13.729716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:13.893169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:14.050165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:14.211161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:14.375995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:14.546715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:14.720705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:14.902096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:15.078299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:15.320650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:15.477923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:15.646708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:15.811970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:15.975002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:16.146994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:16.311374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:16.468240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:16.631570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:16.796578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:16.960402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:17.120198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:17.280169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:17.439047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:17.596599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:17.755296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:17.914876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:18.078063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:18.235341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:18.388487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:18.551907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:18.713090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:18.874979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:19.036658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:19.200475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:19.357922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:19.517077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:19.679312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:19.840074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:19.994956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:20.252814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:20.416626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:20.578814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:20.741098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:20.904551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:21.069407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:21.229857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:21.389492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:21.556461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:21.722677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:21.886215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:22.053400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:22.221035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:22.379732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:22.543814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:22.708244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:22.874007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:23.034663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:23.195366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:23.356827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:23.516091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:23.677940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:23.840909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:24.003513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:24.163982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:24.322959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:24.491957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:24.655913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:24.821324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:24.986467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:25.152670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:25.309635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:25.473140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:25.635756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:25.798719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:25.958874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:26.262455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:26.450156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:26.643987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:26.825856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:26.997458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:27.166853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:27.330960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:27.490341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:27.656786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:27.823417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:27.988361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:28.159068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:28.325431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:28.483520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:28.651394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:28.816495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:28.980244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:29.138714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:29.299740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:29.460063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:29.618074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:29.776561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:29.936024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:30.096146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:30.252505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:30.409886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:30.573337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:30.734764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:30.896933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:31.059512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:31.223533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:31.378015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:31.539492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:31.699656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:31.859960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:04:32.016510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-24T09:05:35.363945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-24T09:05:35.531022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-24T09:05:35.691141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-02-24T09:05:35.840631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-02-24T09:05:09.797745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:05:12.103536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T09:05:15.041344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Sample

First rows

FOLIOTYPEEdit_dtPINDOR_COWNERADDR_1ADDR_2CITYSTATEZIPCOUNTRYSUBSITE_ADDRSITE_CITYSITE_ZIPLEGAL1LEGAL2LEGAL3LEGAL4DBASTRAPtBEDStBATHStSTORIEStUNITStBLDGSTAXDISTJUSTLANDBLDGEXFACTEFFHEAT_ARASD_VALTAX_VALMUNISD1SD2TIFBASES_DATEVIS_AMTACREAGENBHC
000000100002019-04-08A-23-33-15-ZZZ-000000-00020.08800UNITED STATESEGMONT KEY STATE PARK4905 34TH ST S PMB 5000SAINT PETERSBURGFL33711-4511ZZZ0 EGMONT KEYST PETERSBURG3371523 24 25 AND 26-33-15THAT ISLAND KNOWN AS EGMONT KEY DESC AS GOVT LOTS1 2 & 3 IN SEC 23-33-15 GOVT LOT 1 IN SEC 24-33-15GOV LOT 1 IN SEC 25-33-15 & GOV LOTS 1 & 2 IN SECEGMONT KEY ISLAND153323ZZZ000000000200A0.00.00.00.00.0TA5583954.05505854.00.078100.0000.04632324.00.0A0None0.0313.011000227002.0
100000500002019-04-08A-23-33-15-ZZZ-000000-00040.08600HILLSBOROUGH COUNTYREAL ESTATE DEPTPO BOX 1110TAMPAFL33601-1110ZZZ0ST PETERSBURG33715THAT PART OF GOVT LOT 1 DESC AS LYING S 2 DEG 34MIN 04 SEC W 5226 FT FROM CENTER OF EGMONTLIGHTHOUSE SAID CENTER BEING LAT 27 DEG 36 MIN01.980 SEC LONG 82 DEG 45 MIN 39.003 SEC AND RUNEGMONT KEY153323ZZZ000000000400A0.00.00.00.00.0TA240881.0172631.00.068250.0000.0211044.00.0A0None0.06.147920227002.0
200000800002019-04-09U-01-27-17-001-000000-00001.00000PAULINE L SEVIGNY/ LIFE ESTATE19931 ANGEL LNODESSAFL33556-391900119931 ANGEL LNODESSA33556KEYSTONE PARK COLONYA PORTION OF TRACT 1 IN NE 1/4 OF SEC 1 DESC AS:COM AT NW COR OF TRACT 1 S 89 DEG 16 MIN 05 SEC E603.85 FT TO NE COR S 01 DEG 47 MIN 49 SEC E172701001000000000010U0.00.00.00.00.0U207317.0169035.00.038282.0000.0178301.0178301.0U0None0.04.713410211007.0
300000801002019-04-09U-01-27-17-001-000000-00001.10100JEFFERY AND PATRICIA SEVIGNY19859 ANGEL LNODESSAFL33556-391700119859 ANGEL LNODESSA33556KEYSTONE PARK COLONYTRACT 1 IN NE 1/4 LESS N 200 FT THEREOF LESS THECOM AT NW COR OF TRACT 1 S 01 DEG 36 MIN 23 SEC E339.93 FT FOR POB N 88 DEG 51 MIN 52 SEC E 604.43172701001000000000011U3.02.02.01.01.0U565190.0174976.0384856.05358.0199620082617.0418262.0368262.0U20161987-08-01I50000.05.058780211007.0
400000900002019-04-09U-01-27-17-001-000000-00002.00100MARIETTA SHIVER LIFE ESTATE19901 ANGEL LNODESSAFL33556-391900119901 ANGEL LNODESSA33556KEYSTONE PARK COLONYS 210 OF W 225 FT OF TRACT 2 IN NE 1/4 LESS W 15FT FOR R/W172701001000000000020U2.01.01.01.01.0U221664.075750.0136382.09532.0197319971619.0124722.00.0U1994None0.01.000590211007.0
500000901002019-04-09U-01-27-17-001-000000-00002.10100JASEN T AND CHRISTINA J KEYES19913 ANGEL LNODESSAFL33556-391900119913 ANGEL LNODESSA33556KEYSTONE PARK COLONYN 322.24 FT OF TRACT 2 IN NE 1/4 LESS W 15 FT FORRD R/W172701001000000000021U3.02.51.01.01.0U453092.0272419.0169047.011626.0197619981572.0453092.0453092.0U02021-10-27I750000.04.438490211007.0
600000902002019-04-09U-01-27-17-001-000000-00002.20000IRENE K MCKINNEY / LIFE ESTATE10036 LAKE OAK CIRTAMPAFL33624-528800119907 ANGEL LNODESSA33556KEYSTONE PARK COLONYTRACT 2 IN NE 1/4 LESS S 210 FT OF W 225 FT ANDLESS N 322.24 FT AND LESS W 15 FT FOR RD R/W172701001000000000022U0.00.00.00.00.0U234878.0229688.00.05190.0000.0195065.0195065.0U02000-02-03V100000.03.498560211007.0
700001000002019-04-09U-01-27-17-001-000000-00003.00100RICHARD D AND MITZI G DOERR6934 W COUNTY LINE RDODESSAFL33556-39010016934 W COUNTY LINE RDODESSA33556KEYSTONE PARK COLONYW 1/2 OF THE FOLLOWING DESCRIPTION N 300 FT OFTRACT 3 OF NE 1/4 LESS E 305.4 FT172701001000000000030U3.02.01.01.01.0U260068.076500.0133128.050440.0192619732143.0173560.0123560.0U19941988-06-01I52500.00.992559211007.0
800001000012019-04-09U-01-27-17-001-000000-00004.10100** CONFIDENTIAL **7020 COUNTY LINE RDODESSAFL33556-0017020 COUNTY LINE RDUnincorporated99999KEYSTONE PARK COLONYTRACT 4 IN NE 1/4 DESC AS FR NE COR OF SECTIONRUN N 89 DEG 59 MIN 25 SEC W 2475.68 FT THN S 01DEG 45 MIN 41 SEC E 15 FT THN S 89 DEG 59 MIN 25172701001000000000041U3.02.01.01.01.0U379136.0100470.0250471.028195.0200120111919.0210422.0160422.0U20021998-08-01V25000.01.362980211007.0
900001000022019-04-09U-01-27-17-001-000000-00004.20100RICHARD L AND JUDY K OSTING7010 W COUNTY LINE RDODESSAFL33556-39010017010 W COUNTY LINE RDODESSA33556KEYSTONE PARK COLONYTHAT PART OF TRACT 4 IN NE 1/4 FURTHER DESC ASFROM NE COR OF SEC RUN N 89 DEG 59 MIN W 1856.76FT THN S 01 DEG 52 MIN E 15 FT FOR POB THN CONT S172701001000000000042U4.03.01.01.01.0U395192.075865.0287304.032023.0198720032971.0223410.0173410.0U20132012-06-19I272500.01.309540211007.0

Last rows

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51219729000003582019-12-17U-36-27-18-ZZZ-000000-74620.30029PUBLIC LANDSUNKNOWNZZZ0LUTZ33548FAIRCLOTH ESTATES15 FT EASEMENT N OF LOTS 5-13 AS SHOWN IN PB49-26182736ZZZ000000746203U0.00.00.00.00.0U100.0100.00.00.0000.0100.00.0U0None0.00.381217215002.0
51219829000003592019-07-25U-08-27-20-ZZZ-000001-89000.10029PUBLIC LANDSUNKNOWNZZZ0TAMPA33647UNPLATED LAND DESCRIBED AS BEGIN AT NE COR OF LOT16 CROSS CREEK PARCEL 1 THN N 25 FT THN W 265 FTTHN S 25 FT THN E 265 FT TO POB202708ZZZ000001890001U0.00.00.00.00.0U100.0100.00.00.0000.0100.00.0U0None0.00.157790216007.0
51219929000003602021-03-23U-34-28-18-ZZZ-000001-04810.40029PUBLIC LANDSUNKNOWNZZZ0TAMPA33614PARCEL BEG S 366 FT AND W 170.59 FROM NE COR OFSW 1/4 OF NE 1/4 FOR POB THN S 54 FT THN W 15 FTTHN N 54 FT THN E 15 FT TO POB182834ZZZ000001048104U0.00.00.00.00.0U100.0100.00.00.0000.0100.00.0U0None0.00.027108206005.0
51220029000003612019-07-29A-06-29-19-ZZZ-000005-76000.10029PUBLIC LANDSUNKNOWNZZZ0TAMPA33610TRACT BEG 117 FT AND 130 FT S 0F NE COR OF NW 1/4OF NW 1/4 OF NE 1/4 OF SE 1/4 AND RUN W 101.62 FTFOR POB CONT W 88 FT THN S 32.5 FT THN E 88 FT THNN 32.5 FT TO POB192906ZZZ000005760001A0.00.00.00.00.0TA100.0100.00.00.0000.0100.00.0A90None0.00.065165205006.0
51220129000003622019-07-29U-18-28-21-ZZZ-000003-61540.10029PUBLIC LANDSUNKNOWNZZZ0THONOTOSASSA33592UNIDENTIFIED RIGHT OF WAY ABUTTING BRANDY BRYAN RDIN SW 1/4 OF NE 1/4 OF NW 1/4212818ZZZ000003615401U0.00.00.00.00.0U100.0100.00.00.0000.0100.00.0U0None0.00.790215219003.0
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51220329000003642020-01-27U-06-28-22-ZZZ-000004-63330.10029PUBLIC LANDSUNKNOWNZZZ0PLANT CITY33565PORTION OF MCLIN DR IN SE 1/4222806ZZZ000004633301U0.00.00.00.00.0U100.0100.00.00.0000.0100.00.0U0None0.01.705740219003.0
51220429000003662020-08-24U-13-27-18-ZZZ-000000-54350.28610HILLSBOROUGH COUNTYREAL ESTATE DEPTPO BOX 1110TAMPAFL33601-1110ZZZ0LUTZ33548N 25 FT OF THE E 15 FT OF THAT PART OF S 440 FT OFNE 1/4 OF NW 1/4 W OF RR --- S 25 FT OF E 15 FT OFTHAT PART OF N 440 FT OF S 880 FT OF NE 1/4 OFNW 1/4 W OF RR182713ZZZ000000543502U0.00.00.00.00.0U100.0100.00.00.0000.0100.00.0U0None0.00.017145215012.0
51220529000003672021-12-08A-20-29-18-3KX-000001-0PARK.00029PUBLIC LANDSUNKNOWN3KX0TAMPA33609ROSEMONT SUBDIVISIONTRACTS MARKED PARK1829203KX0000010PARK0A0.00.00.00.00.0TA100.0100.00.00.0000.0100.00.0A0None0.00.412655202008.0
512206NEW2021-12-020.00.00.00.00.00.00.00.00.0000.00.00.00None0.08.6637700.0